Misbehavior detection in autonomous driving communications

ABSTRACT

A first roadway system receives a communication from a second roadway system over a wireless channel, where the communication includes a description of a physical object within a driving environment. Characteristics of the physical object are determined based on sensors of the first roadway system. The communication is determined to contain an anomaly based on a comparison of the description of the physical object with the characteristics determined based on the sensors of the first roadway system. Misbehavior data is generated to describe the anomaly. A remedial action is initiated based on the anomaly.

RELATED APPLICATIONS

This application claims benefit to U.S. Provisional Patent ApplicationSer. No. 62/812,509, filed Mar. 1, 2019 and incorporated by referenceherein in its entirety.

TECHNICAL FIELD

This disclosure relates in general to the field of computer systems and,more particularly, to computing systems facilitating communicationbetween autonomous vehicles.

BACKGROUND

Some vehicles are configured to operate in an autonomous mode in whichthe vehicle navigates through an environment with little or no inputfrom a driver. Such a vehicle typically includes one or more sensorsthat are configured to sense information about the environment. Thevehicle may use the sensed information to navigate through theenvironment. For example, if the sensors sense that the vehicle isapproaching an obstacle, the vehicle may navigate around the obstacle.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a simplified block diagram of an example driving environment.

FIG. 2 is a simplified block diagram of an example in-vehicle automateddriving system.

FIG. 3 is a simplified block diagram illustrating automated drivinglevels.

FIG. 4 is a simplified block diagram illustrating operating principlesof an automated driving system.

FIG. 5 is a simplified block diagram illustrating basic functions ofautomated driving systems.

FIG. 6 is a simplified block diagram illustrating components of anexample automated driving system.

FIG. 7 is a simplified diagram illustrating vehicles within an exampledriving environment.

FIG. 8 is a simplified diagram illustrating an example ofcommunication-based misbehavior within an autonomous drivingenvironment.

FIG. 9 illustrates diagrams showing the relative attackable areasfacilitated in different example autonomous driving systems.

FIG. 10 illustrates diagrams showing an example of the changing statesof vehicles in communication with one another over time.

FIG. 11 is a simplified block diagram illustrating an example flow fordetecting anomalies by a roadway system.

FIG. 12 illustrates diagrams showing different degrees of inconsistency,which may be measured by a roadway system when assessingcommunication-based misbehavior by another roadway system within anautonomous driving environment.

FIG. 13 is a simplified diagram illustrating an example of an attemptedghost vehicle attack.

FIGS. 14A-14B illustrate a simplified block diagram illustrating exampledetection of communication-based misbehavior within an autonomousdriving environment.

FIG. 15 illustrates diagrams showing differing fields of views of areported object by different roadway systems within an autonomousdriving environment.

FIG. 16 is a simplified diagram illustrating an example certificatemanagement architecture.

FIG. 17 is a simplified flow diagram illustrating an example techniquefor detecting communication-base misbehavior by a roadway system withinan autonomous driving environment.

FIG. 18 is a block diagram of an exemplary processor in accordance withone embodiment.

FIG. 19 is a block diagram of an exemplary computing system inaccordance with one embodiment.

Like reference numbers and designations in the various drawings indicatelike elements.

DESCRIPTION OF EXAMPLE EMBODIMENTS

FIG. 1 is a simplified illustration 100 showing an example autonomousdriving environment. Vehicles (e.g., 105, 110, 115, etc.) may beprovided with varying levels of autonomous driving capabilitiesfacilitated through in-vehicle computing systems with logic implementedin hardware, firmware, and/or software to enable respective autonomousdriving stacks. Such autonomous driving stacks may allow vehicles toself-control or provide driver assistance to detect roadways, navigatefrom one point to another, detect other vehicles and road actors (e.g.,pedestrians (e.g., 135), bicyclists, etc.), detect obstacles and hazards(e.g., 120), and road conditions (e.g., traffic, road conditions,weather conditions, etc.), and adjust control and guidance of thevehicle accordingly.

In some implementations, vehicles (e.g., 105, 110, 115) within theenvironment may be “connected” in that the in-vehicle computing systemsinclude communication modules to support wireless communication usingone or more technologies (e.g., IEEE 802.11 communications (e.g., WiFi),cellular data networks (e.g., 3rd Generation Partnership Project (3GPP)networks, Global System for Mobile Communication (GSM), general packetradio service, code division multiple access (CDMA), etc.), Bluetooth™,millimeter wave (mmWave), ZigBee™, Z-Wave™, etc.), allowing thein-vehicle computing systems to connect to and communicate with othercomputing systems, such as the in-vehicle computing systems of othervehicles, roadside units, cloud-based computing systems, or othersupporting infrastructure. For instance, in some implementations,vehicles (e.g., 105, 110, 115) may communicate with computing systemsproviding sensors, data, and services in support of the vehicles' ownautonomous driving capabilities. For instance, as shown in theillustrative example of FIG. 1, supporting drones 180 (e.g.,ground-based and/or aerial), roadside computing devices (or road sideunits (RSUs) (e.g., 140), various external (to the vehicle, or“extraneous”) sensor devices (e.g., 160, 165, 170, 175, etc.), and otherdevices may be provided as autonomous driving infrastructure separatefrom the computing systems, sensors, and logic implemented on thevehicles (e.g., 105, 110, 115) to support and improve autonomous drivingresults provided through the vehicles, among other examples. Vehiclesmay also communicate with other connected vehicles over wirelesscommunication channels to share data and coordinate movement within anautonomous driving environment, among other example communications.

As illustrated in the example of FIG. 1, autonomous drivinginfrastructure may incorporate a variety of different systems. Suchsystems may vary depending on the location, with more developed roadways(e.g., roadways controlled by specific municipalities or tollauthorities, roadways in urban areas, sections of roadways known to beproblematic for autonomous vehicles, etc.) having a greater number ormore advanced supporting infrastructure devices than other sections ofroadway, etc. For instance, supplemental sensor devices (e.g., 160, 165,170, 175) may be provided, which include sensors for observing portionsof roadways and vehicles moving within the environment and generatingcorresponding data describing or embodying the observations of thesensors. As examples, sensor devices may be embedded within the roadwayitself (e.g., sensor 160), on roadside or overhead signage (e.g., sensor165 on sign 125), sensors (e.g., 170, 175) attached to electronicroadside equipment or fixtures (e.g., traffic lights (e.g., 130),electronic road signs, electronic billboards, etc.), dedicated road sideunits (e.g., 140), among other examples. Sensor devices may also includecommunication capabilities to communicate their collected sensor datadirectly to nearby connected vehicles or to fog- or cloud-basedcomputing systems (e.g., 140, 150). Vehicles may obtain sensor datacollected by external sensor devices (e.g., 160, 165, 170, 175, 180), ordata embodying observations or recommendations generated by othersystems (e.g., 140, 150) based on sensor data from these sensor devices(e.g., 160, 165, 170, 175, 180), and use this data in sensor fusion,inference, path planning, and other tasks performed by the in-vehicleautonomous driving system. In some cases, such extraneous sensors andsensor data may, in actuality, be within the vehicle, such as in theform of an after-market sensor attached to the vehicle, a personalcomputing device (e.g., smartphone, wearable, etc.) carried or worn bypassengers of the vehicle, etc. Other road actors, includingpedestrians, bicycles, drones, electronic scooters, etc., may also beprovided with or carry sensors to generate sensor data describing anautonomous driving environment, which may be used and consumed byautonomous vehicles, cloud- or fog-based support systems (e.g., 140,150), other sensor devices (e.g., 160, 165, 170, 175, 180), among otherexamples.

As autonomous vehicle systems may possess varying levels offunctionality and sophistication, support infrastructure may be calledupon to supplement not only the sensing capabilities of some vehicles,but also the computer and machine learning functionality enablingautonomous driving functionality of some vehicles. For instance, computeresources and autonomous driving logic used to facilitate machinelearning model training and use of such machine learning models may beprovided on the in-vehicle computing systems entirely or partially onboth the in-vehicle systems and some external systems (e.g., 140, 150).For instance, a connected vehicle may communicate with road-side units,edge systems, or cloud-based devices (e.g., 140) local to a particularsegment of roadway, with such devices (e.g., 140) capable of providingdata (e.g., sensor data aggregated from local sensors (e.g., 160, 165,170, 175, 180) or data reported from sensors of other vehicles),performing computations (as a service) on data provided by a vehicle tosupplement the capabilities native to the vehicle, and/or pushinformation to passing or approaching vehicles (e.g., based on sensordata collected at the device 140 or from nearby sensor devices, etc.). Aconnected vehicle (e.g., 105, 110, 115) may also or instead communicatewith cloud-based computing systems (e.g., 150), which may providesimilar memory, sensing, and computational resources to enhance thoseavailable at the vehicle. For instance, a cloud-based system (e.g., 150)may collect sensor data from a variety of devices in one or morelocations and utilize this data to build and/or train machine-learningmodels which may be used at the cloud-based system (to provide resultsto various vehicles (e.g., 105, 110, 115) in communication with thecloud-based system 150, or to push to vehicles for use by theirin-vehicle systems, among other example implementations. Access points(e.g., 145), such as cell-phone towers, road-side units, network accesspoints mounted to various roadway infrastructure, access points providedby neighboring vehicles or buildings, and other access points, may beprovided within an environment and used to facilitate communication overone or more local or wide area networks (e.g., 155) between cloud-basedsystems (e.g., 150) and various vehicles (e.g., 105, 110, 115). Throughsuch infrastructure and computing systems, it should be appreciated thatthe examples, features, and solutions discussed herein may be performedentirely by one or more of such in-vehicle computing systems, fog-basedor edge computing devices, or cloud-based computing systems, or bycombinations of the foregoing through communication and cooperationbetween the systems.

In general, “servers,” “clients,” “computing devices,” “networkelements,” “hosts,” “platforms”, “sensor devices,” “edge device,”“autonomous driving systems”, “autonomous vehicles”, “fog-based system”,“cloud-based system”, and “systems” generally, etc. discussed herein caninclude electronic computing devices operable to receive, transmit,process, store, or manage data and information associated with anautonomous driving environment. As used in this document, the term“computer,” “processor,” “processor device,” or “processing device” isintended to encompass any suitable processing apparatus, includingcentral processing units (CPUs), graphical processing units (GPUs),application specific integrated circuits (ASICs), field programmablegate arrays (FPGAs), digital signal processors (DSPs), tensor processorsand other matrix arithmetic processors, among other examples. Forexample, elements shown as single devices within the environment may beimplemented using a plurality of computing devices and processors, suchas server pools including multiple server computers. Further, any, all,or some of the computing devices may be adapted to execute any operatingsystem, including Linux™, UNIX™, Microsoft™ Windows™, Apple™ macOS™,Apple™ iOS™, Google™ Android™, Windows Server ™, etc., as well asvirtual machines adapted to virtualize execution of a particularoperating system, including customized and proprietary operatingsystems.

Any of the flows, methods, processes (or portions thereof) orfunctionality of any of the various components described below orillustrated in the figures may be performed by any suitable computinglogic, such as one or more modules, engines, blocks, units, models,systems, or other suitable computing logic. Reference herein to a“module”, “engine”, “block”, “unit”, “model”, “system” or “logic” mayrefer to hardware, firmware, software and/or combinations of each toperform one or more functions. As an example, a module, engine, block,unit, model, system, or logic may include one or more hardwarecomponents, such as a micro-controller or processor, associated with anon-transitory medium to store code adapted to be executed by themicro-controller or processor. Therefore, reference to a module, engine,block, unit, model, system, or logic, in one embodiment, may refer tohardware, which is specifically configured to recognize and/or executethe code to be held on a non-transitory medium. Furthermore, in anotherembodiment, use of module, engine, block, unit, model, system, or logicrefers to the non-transitory medium including the code, which isspecifically adapted to be executed by the microcontroller or processorto perform predetermined operations. And as can be inferred, in yetanother embodiment, a module, engine, block, unit, model, system, orlogic may refer to the combination of the hardware and thenon-transitory medium. In various embodiments, a module, engine, block,unit, model, system, or logic may include a microprocessor or otherprocessing element operable to execute software instructions, discretelogic such as an application specific integrated circuit (ASIC), aprogrammed logic device such as a field programmable gate array (FPGA),a memory device containing instructions, combinations of logic devices(e.g., as would be found on a printed circuit board), or other suitablehardware and/or software. A module, engine, block, unit, model, system,or logic may include one or more gates or other circuit components,which may be implemented by, e.g., transistors. In some embodiments, amodule, engine, block, unit, model, system, or logic may be fullyembodied as software. Software may be embodied as a software package,code, instructions, instruction sets and/or data recorded onnon-transitory computer readable storage medium. Firmware may beembodied as code, instructions or instruction sets and/or data that arehard-coded (e.g., nonvolatile) in memory devices. Furthermore, logicboundaries that are illustrated as separate commonly vary andpotentially overlap. For example, a first and second module (or multipleengines, blocks, units, models, systems, or logics) may share hardware,software, firmware, or a combination thereof, while potentiallyretaining some independent hardware, software, or firmware.

The flows, methods, and processes described below and in theaccompanying figures are merely representative of functions that may beperformed in particular embodiments. In other embodiments, additionalfunctions may be performed in the flows, methods, and processes. Variousembodiments of the present disclosure contemplate any suitable signalingmechanisms for accomplishing the functions described herein. Some of thefunctions illustrated herein may be repeated, combined, modified, ordeleted within the flows, methods, and processes where appropriate.Additionally, functions may be performed in any suitable order withinthe flows, methods, and processes without departing from the scope ofparticular embodiments.

As introduced above, inter-device communication may be utilized toenable multiple vehicles, as well as road side unit (RSU) systems tocooperate and leverage one another's functionality to enhance andfacilitate at least semi-autonomous driving environments. For instance,V2X (vehicles-to-everything) communication enables information sharingbetween vehicles, pedestrians and roadside units in the proximitythrough V2X messages. As such, V2X becomes may serve as a key enablerfor intelligent transportation systems and its usage spans fromsafety-critical applications to vehicular infotainment systems, fromlocal cooperative driving to large scale traffic management. While suchcommunication schemes may be beneficial to facilitating autonomousdriving, such machine-to-machine communications (e.g., V2X) may bevulnerable to attacks or threats. For instance, many V2X applications(e.g., collision avoidance application, intelligent traffic controlsignal systems) may rely on exchanging driving information (e.g., speed,location, acceleration, and heading) via Basic Safety Messages (BSMs)(e.g., in the United States) or Cooperative Awareness Messages (CAMs)(e.g., in Europe) (referred to herein collectively as basic safetymessages (BSMs). Accordingly, security breaches and attacks of suchmessages may potentially lead to catastrophic safety events and threatenthe public trust in such systems.

As one example, autonomous driving systems may support sharedperception, with vehicles or road side units equipped with sensors andmachine learning-based perception (e.g., computer vision) capabilitiesthat enable machine detection of objects in a driving environment. V2Xor other messages may be communicated between roadway systems associatedwith various roadway actors (e.g., vehicles, road side units,pedestrians/bikers carrying computing devices, etc.), allowing objectdetection and positioning to be shared between roadway systems tosupplement what (if any) perception logic is implemented on thereceiving system. A vehicle may then rely on representations by otherroadway systems that various objects are present (or not present) basedon messages received from other roadway actor systems.

Authenticity and integrity of messages exchanged over V2X is guaranteedby digital signatures. For instance, a computing system of a vehicle orroad side unit issuing such messages may sign every message sent using asecret key associated to a given certificate. The receiving system canverify the authenticity and integrity of any message by verifying thevalidity of the certificate, and of the signature. Such certificates maybe managed and issued by a trusted certificate authority. For instance,V2X certificates may be supplied to the legitimate users by acredentials management infrastructure, such as the Secure CredentialsManagement System (SCMS) in the United States, among other exampleentities.

While shared perception implementations through V2X has the potential ofextending the perception of vehicles with sensors and capable of V2Xcommunication (“Connected Autonomous Vehicles (CAVs)”) as well asvehicles without sensors and capable of V2X communication (“ConnectedVehicles (CVs)”) (vehicles without sensors, but capable of V2Xcommunication). Different from the simple CV case, where vehicles sharetheir pose, speed, and other characteristics of the sending entity inV2X basic safety messages, in a shared perception system, roadwaysystems may exchange perception information (e.g., the list of objectsthey detected and tracked). Shared perception may extend the autonomousdriving capabilities based on perception to lower level autonomousvehicles. In spite of these example benefits, messages in a V2X systemmay be vulnerable to malicious actors who may mount fake data attacks tointerfere with the correct operation of vehicles on a segment ofroadway. While V2X (and other messages) may be integrity andauthenticity protected using digital signatures, a sophisticatedadversary that possesses valid V2X credentials may nonetheless mountfake data attacks using compromised V2X messages. For instance, anattacker may launch a ghost vehicle attack (GVA) in a shared perceptionenvironment, where a malicious sender creates a non- existing ordeliberately misplaced object and communicates the same using V2X toinfluence the perception of the environment of other vehicles. Theeffects of this attack may be significant for the safety of thevehicles. Accordingly, in this disclosure, improved roadway systemimplementations are described including features to detect and mitigateagainst misbehavior involving V2X and other inter-roadway systemcommunications.

With reference now to FIG. 2, a simplified block diagram 200 is shownillustrating an example implementation of a roadway system 205 equippedwith a driving perception system 210 and misbehavior detection engine215. Such a system 205 may be implemented in any one of a variety ofroadway actors, such as vehicles (e.g., 110, 115), roadside units (e.g.,160, 165), drones, among other entities at or near a roadway. In oneexample, a roadways system 205 may include one or more processors 202,such as central processing units (CPUs), graphical processing units(GPUs), application specific integrated circuits (ASICs), fieldprogrammable gate arrays (FPGAs), digital signal processors (DSPs),tensor processors and other matrix arithmetic processors, among otherexamples. Such processors 202 may further include or be coupled tointegrated hardware accelerators, which may provide further hardware toaccelerate certain processing and memory access functions, such asfunctions relating to machine learning inference or training (includingany of the machine learning inference or training described below),processing of particular sensor data (e.g., camera image data, LightDetecting and Ranging (LIDAR) point clouds, etc.), performing certainarithmetic functions pertaining to autonomous driving (e.g., matrixarithmetic, convolutional arithmetic, etc.), among other examples. Oneor more memory elements (e.g., 206) may be provided to storemachine-executable instructions implementing all or a portion of any oneof the modules or sub-modules of an autonomous driving stack implementedon the vehicle, as well as storing data used to perform perception ofobjects within a driving environment. For instance, such data mayinclude machine learning models (e.g., 256), sensor data (e.g., 258),object data 262 (e.g., determined locally at the roadway system 205 orreceived from other roadways systems via V2X messaging), and other datareceived, generated, or used in connection with autonomous drivingfunctionality to be performed by the vehicle (or used in connection withthe examples and solutions discussed herein). Various communicationmodules (e.g., 212) may also be provided, implemented in hardwarecircuitry and/or software to implement communication capabilities usedby the vehicle's system to communicate with other extraneous computingsystems over one or more network channels employing one or more networkcommunication technologies (e.g., V2X). These various processors 202,accelerators, memory devices 206, and network communication modules 212,may be interconnected on the vehicle system through a variety ofinterfaces implemented, for instance, through one or more interconnectfabrics or links, such as fabrics utilizing technologies such as aPeripheral Component Interconnect Express (PCIe), Ethernet, UniversalSerial Bus (USB), Ultra Path Interconnect (UPI), Controller Area Network(CAN) bus, among others.

In the case of a vehicle, a roadway system 205 may generate observationsand corresponding data that may be utilized as inputs to an in-vehicledriving system to influence the manner in which the driving of thevehicle is controlled. In roadside units, the roadway system 205 maysimilarly detect and determine behavior of various objects within aportion of a driving environment and communicate these observations tovehicles within the driving environment for their use, as well as tobackend systems for further analysis, reporting, security, lawenforcement, regulatory and legal compliance, and other example uses. Inone implementation, a driving perception system 210 may be provided withmachine executable logic (e.g., implemented in hardware and/or software)to “perceive” surrounding objects and events associated with theseobjects within a driving environment, for instance, using machinelearning. Accordingly, a machine learning engine 232 may be provided toutilize various machine learning models (e.g., 256) provided at theroadway system 205 to take as inputs sensor data (e.g., 258) collectedby local or extraneous sensors (e.g., 225) and generate inferences basedon the trained machine learning models (e.g., 256). Such inferences mayform the basis for the perception of a particular object (e.g., torecognize that the object exists and classify the object as a particulartype of object (e.g., a car, truck, pedestrian, road sign, bicycle,animal or other road hazard, etc.). Such machine learning models 256 mayinclude artificial neural network models, convolutional neural networks,decision tree-based models, support vector machines (SVMs), Bayesianmodels, deep learning models, and other example models. In someimplementations, an example machine learning engine 232 may furtherinclude logic to participate in training (e.g., initial training,continuous training, etc.) of one or more of the machine learning models256. In some embodiments, the machine learning model training orinference described herein may be performed off-vehicle, among otherexample implementations.

The machine learning engine(s) 232 provided at the vehicle may beutilized to support and provide results for use by other logicalcomponents and modules of the driving perception system 210 implementingan autonomous driving stack and other autonomous-driving-relatedfeatures. For instance, a data collection module 234 may be providedwith logic to determine sources from which data is to be collected(e.g., for inputs in the training or use of various machine learningmodels 256 used by the vehicle). For instance, the particular source(e.g., internal sensors (e.g., 225) or extraneous sources (communicatingover wireless channels)) may be selected, as well as the frequency andfidelity at which the data may be sampled is selected. In some cases,such selections and configurations may be made at least partiallyautonomously by the data collection module 234 using one or morecorresponding machine learning models (e.g., to collect data asappropriate given a particular detected scenario).

A sensor fusion module 236 may also be used to govern the use andprocessing of the various sensor inputs utilized by the machine learningengine 232 and other modules (e.g., 238, 240, 242, etc.) of the drivingperception system 205. One or more sensor fusion modules (e.g., 236) maybe provided, which may derive an output from multiple sensor datasources (e.g., on the vehicle or extraneous to the vehicle). The sourcesmay be homogenous or heterogeneous types of sources (e.g., multipleinputs from multiple instances of a common type of sensor, or frominstances of multiple different types of sensors). An example sensorfusion module 236 may apply direct fusion, indirect fusion, among otherexample sensor fusion techniques. The output of the sensor fusion may,in some cases by fed as an input (along with potentially additionalinputs) to another module of the driving perception system and/or one ormore machine learning models in connection with providing autonomousdriving functionality or other functionality, such as described in theexample solutions discussed herein.

A perception engine 238 may be provided in some examples, which may takeas inputs the results of inferences performed by machine learning engine232 and/or various sensor data (e.g., 258) including data, in someinstances, from extraneous sources and/or sensor fusion module 236 toperform object recognition and/or tracking of detected objects, amongother example functions corresponding to autonomous perception of theenvironment encountered (or to be encountered) by the roadway system205. Perception engine 238 may perform object recognition from sensordata inputs using deep learning, such as through one or moreconvolutional neural networks and other machine learning models 256.Object tracking may also be performed to autonomously estimate, fromsensor data inputs, whether an object is moving and, if so, along whattrajectory. For instance, after a given object is recognized, aperception engine 238 may detect how the given object moves in relationto the vehicle. Such functionality may be used, for instance, to detectobjects such as other vehicles, pedestrians, wildlife, cyclists, etc.moving within an environment, which may affect the path of the vehicleon a roadway, among other example uses.

A localization engine 240 may also be included within driving perceptionsystem 210 in some implementation. In some cases, localization engine240 may be implemented as a sub-component of a perception engine 238 andidentify coordinates or other location information (e.g., relativedistance between objects) for objects detected in the environment. Thelocalization engine 240 may also make use of one or more machinelearning models 256 and sensor fusion (e.g., of LIDAR and GPS data,etc.) to determine a high confidence location of the vehicle and thespace it occupies within a given physical space (or environment).

A driving perception system (e.g., 210) may further include a pathplanner 242, which may make use of the results of various other modules,such as data collection 234, sensor fusion 236, perception engine 238,and localization engine (e.g., 240) among others (e.g., recommendationengine 244) to determine a path plan and/or action plan where thedriving perception system 210 is implemented on a vehicle, which may beused by actuate controls to automate driving functionality of thevehicle within an environment. In the case of a roadside unit, a pathplanner 242 may utilize perceptions determined for various movingobjects within an environment and predict (e.g., based on other objectsdetected within the environment or other determined roadway conditions(e.g., weather, light conditions, etc.) how a particular object (e.g., avehicle, a pedestrian, a bike, etc.) is likely to behave and move withinthe environment in the immediate future. For instance, a path planner242 may utilize sensor data, other machine learning models, and inputsprovided by other modules of the driving perception system 210 todetermine probabilities of various events within a driving environmentto determine effective real-time plans or predicted behaviors within theenvironment.

As discussed above, a roadway system (e.g., 205) may utilize a varietyof sensor data (e.g., 258) generated by various sensors provided on andexternal to the vehicle. As an example, a vehicle may possess an arrayof sensors (e.g., 225) to collect various information relating to theexterior of the vehicle and the surrounding environment, vehicle systemstatus, conditions within the vehicle, and other information usable bythe modules of the vehicle's driving perception system 210. Forinstance, such sensors 225 may include global positioning (GPS) sensors,light detection and ranging (LIDAR) sensors, two-dimensional (2D)cameras, three-dimensional (3D) or stereo cameras, acoustic sensors,inertial measurement unit (IMU) sensors, thermal sensors, ultrasoundsensors, bio sensors (e.g., facial recognition, voice recognition, heartrate sensors, body temperature sensors, emotion detection sensors,etc.), radar sensors, weather sensors (not shown), among other examplesensors. Similar sensors may be utilized in roadside units. Indeed,sensor data 258 may be generated by sensors that are not integrallycoupled to the vehicle, including sensors on other vehicles (e.g., 115)(which may be communicated through vehicle-to-vehicle communications orother techniques), sensors on ground-based or aerial drones, sensors ofuser devices (e.g., a smartphone or wearable) carried by human usersinside or outside a vehicle, and sensors mounted or provided with otherroadside elements, such as another roadside unit (e.g., 160), road sign,traffic light, streetlight, etc. Sensor data from such extraneous sensordevices may be provided directly from the sensor devices to the vehicleor may be provided through data aggregation devices or as resultsgenerated based on these sensors by other computing systems, among otherexample implementations.

In some implementations, an autonomous vehicle or roadside unit mayinterface with and leverage information and services provided by othercomputing systems to enhance, enable, or otherwise support theperception functionality of the system. In some instances, someautonomous driving features (including some of the example solutionsdiscussed herein) may be enabled through services, computing logic,machine learning models, data, or other resources of computing systemsexternal to a vehicle. When such external systems are unavailable to avehicle, it may be that these features are at least temporarilydisabled. For instance, external computing systems may be provided andleveraged, which are hosted in road-side units or fog-based edgedevices, other (e.g., higher-level) vehicles (e.g., 115), andcloud-based systems (e.g., accessible through various networks (e.g.,290)). A roadside unit or cloud-based system (or other cooperatingsystem, with which a vehicle (e.g., 115) interacts may include all or aportion of the logic typically implemented in an example in-vehicleautomated driving system (e.g., including driving perception system210), along with potentially additional functionality and logic. Forinstance, a cloud-based computing system, road side unit 140, or othercomputing system may include a machine learning engine supporting eitheror both model training and inference engine logic. For instance, suchexternal systems may possess higher-end computing resources and moredeveloped or up-to-date machine learning models, allowing these servicesto provide superior results to what would be generated natively on avehicle's automated driving system. For instance, an automated drivingsystem may rely on the machine learning training, machine learninginference, and/or machine learning models provided through a cloud-basedservice for certain tasks and handling certain scenarios. Indeed, itshould be appreciated that one or more of the modules discussed andillustrated as belonging to vehicle may, in some implementations, bealternatively or redundantly provided within a cloud-based, fog-based,or other computing system supporting an autonomous driving environment.

Continuing with the example of FIG. 2, an example roadway system (e.g.,205) may include a misbehavior detection engine 215 to detect instanceswhere messages communicated by other roadway systems (e.g., of a nearbyvehicle (e.g., 110, 115) or roadside unit (e.g., 160, 165) may have beencompromised and should not be trusted and used by the roadway system205. An example misbehavior detection engine 215 may include variouscomponents to enable various modes of misbehavior detection. Forinstance, a misbehavior detection engine 215 may include a messageanalysis engine 220 with logic to scan messages (e.g., V2x) receivedfrom other roadway systems to detect inconsistencies with either or boththe format or content of the messages suggesting an error or maliciousintent in the messages. For instance, message analysis engine 220 maydetect instances where the same sender sends the same object twice,perform range checks (e.g., to detect acceleration outside predefinedboundaries, sudden appearance/teleporting of an object as reported inbasic safety message, etc.). In this sense, a message analysis engine220 may detect potential misbehavior by another roadway system solelyfrom the content and/or format of the received message. Accordingly,even vehicles without in-vehicle perception systems may include andutilize a message analysis engine to detect potential misbehaviorutilizing V2X messaging.

In some implementations, a misbehavior detection engine 215 mayadditionally include a message prediction engine 222 to determinepotential misbehavior by determining that content of a received messageincludes information that deviates from information expected to becontained in the message (e.g., based on information included inprevious messages from the same (or a different) roadway system). Forinstance, the message prediction engine 222 may maintain and utilize oneor more data models modeling behavior of vehicles and other objects thatmay move within a driving environment. The message prediction engine 222may additionally store records of previously received messages fromvarious other roadway systems describing objects within an environment.Based on the previously received messages and the model, an examplemessage prediction engine 222 can predict (e.g., within a range)information to be contained in a subsequent message from another or thesame roadway system describing position of a particular object. If asubsequent message describes the particular object as having a locationor characteristics outside of the predicted range, the messageprediction engine 222 may determine that an incident of potentialmisbehavior has been detected and may continue to monitor messages fromthe source of the subsequent message (and/or preceding message(s)) foradditional anomalies. For instance, a particular roadway systemassociated with a corresponding certificate or identifier may be trackedin watchlist data (e.g., 268), which identifies the roadway systems fromwhich detected anomalous messages originated.

An example misbehavior detection engine 215 may additionally include anoverlap detector 224, which may track the various objects detected anddescribed in received basic safety messages (and other V2X message).More particularly, the overlap detector 224 may track the location of aset of objects as reported in an environment and determine an anomaly ina received basic safety message that identifies an object thatreportedly overlaps with another different object in the same physicalspace (e.g., the message identifies a first automobile occupying aparticular space as a truck reported in another message by anotherroadway system, among other examples). Such overlaps may indicate amalfunction by another roadway system, or more maliciously, an attemptedattack, such as a host vehicle attack.

At a roadway system (e.g., 205) each object detected by the system(e.g., using driving perception system 210 and sensors 225) and/orreported as detected by other roadway systems may be tracked usingcorresponding track data 264. Such track data may be utilized by theoverlap detector 224 to track the various objects reported in basicsafety messages communicated to the roadway system 205. In someimplementations, track data may differentiate or identify the source ofthe information, for instance, whether it was reported in a message fromanother roadway system, detected using the local system's perceptionsystem 210, or both (where the local driving perception system 210confirms the validity of basic safety message that describe conditionsconsistent with what is observed using the local sensors 225). Indeed, amisbehavior detection engine 215 in a roadway system including sensors(e.g., 225) perception logic (e.g., 210) may also include a perceptionanalysis engine 226, which utilizes the perception system 210 of theroadway system to validate objects reported by other roadway systems(e.g., of entities 110, 115, 160, 165, etc.). For instance, a basicsafety message from a particular external roadway system may identifythe presence or absence of a particular object within a particular spaceobservable using the local perception system 210 of a roadway system205. The perception analysis engine 226 may detect that the messagereports an object that overlaps with another object detected usingdriving perception system 210 or an object occupying space the drivingperception system 210 can confirm is free, and flag the message ananomalous, based on this determination.

As introduced above, as messages are detected with anomalous content,the source of the messages may be flagged as potentially compromised ormalicious. A watchlist (embodied in watchlist data 268) may bemaintained to track other roadway systems, which the roadway system 205has detected (e.g., using local misbehavior detection logic (e.g., 220,222, 224, 226)) as the source of a suspicious message. As not allanomalies may be malicious and anomalies may instead result fromoutlying errors in the sensors, perception logic, and/or communicationchannels used by the source roadway system, in some cases, the sendingof a single anomalous safety message may not immediately remedialaction. However, through watchlist data 268, multiple instances ofanomalous data originating from a given source roadway system may resultin future messages from this roadway system being untrusted. Forinstance, watchlist data 268 may include blacklists, which identifyspecific roadway systems, which through multiple suspicious messages,have been deemed to be compromised and untrustworthy. Accordingly, aroadway system 205 may ignore information in messages detected asoriginating from such blacklisted sources. Further, an examplemisbehavior detection engine 215 may include a misbehavior reportingengine (e.g., 228) to report detected misbehavior to other roadwaysystems (e.g., through V2X messages to neighboring roadway actors (e.g.,110, 115, 160, 165) and/or to backend systems (e.g., 250), such ascertificate authorities responsible for maintaining certificates grantedto and used by roadway systems to authenticate their reliability.

In some implementations, an example misbehavior detection engine 215 mayboth send reports of detected anomalous messages to other systems, aswell as receive similar anomalous message reports (or “misbehaviorreports”) from nearby roadway systems (e.g., on 110, 115, 160, 165,etc.) using respective misbehavior detection logic. Instances ofmisbehavior detected by a collection of roadway systems may be trackedat individual roadway systems (e.g., in watchlist data 268) andexternal, administrative systems (e.g., misbehavior authority system250). In some implementations, an example misbehavior detection engine215 may include a collaborative anomaly detection engine 230, which mayfacilitate sharing of misbehavior reports between systems. In someimplementations, when the number of misbehavior reports reported bynearby systems identifying anomalous behavior by a particular roadwaysystem exceeds a threshold (e.g., a number or rate of anomalousidentifications of a particular object, a number or rate of safetymessages sent by a particular roadway system that include anomalousinformation, etc.). Indeed, the collaborative anomaly detection engine230 may determine, from other roadway systems' messages, that aparticular message received from a given roadway system cannot betrusted, even when the particular message is the first that has beenreceived from this system and cannot be verified by the drivingperception system 210 of the roadway system 205, among other examplefeatures.

While a misbehavior detection engine 215 may determine a local blacklistof roadway systems identifiers (e.g., certificates) that are untrusted(and should be ignored), this may not prevent the same malicious roadwaysystems from interfering with the operation of other vehicles androadside units by sharing falsified safety messages (e.g., V2X BSMs). Insome implementations, blacklists may also be shared between roadwaysystems to help broadcast the presence of a malicious actor. Forinstance, a roadway system 205 may receive multiple blacklists shared bymultiple other roadway systems and identify repeating roadway systemidentifiers in the blacklists. In one example, if a given roadway systemidentifier is repeated in a number of shared blacklists, the roadwaysystem 205 may elect to add the roadway system identifier to a localwatchlist maintained by the roadway system (even if that roadway systemhas not been encountered during operation), among other exampleimplementations.

In some implementations, a malicious actor may be more permanentlystifled by invoking a misbehavior authority system 250, such as a systemassociated with a governmental agency, law enforcement, issuer ofroadway system certificates, etc. As illustrated in FIG. 2, an examplemisbehavior authority system 250 may include one or more data processors(e.g., 272), one or more memory elements (e.g., 274), and logicimplemented in hardware and/or software to assist in remediating misuseof V2X communication systems and associated certificates. For instance,a certificate manager 270 may be implemented to manage issuance andinformation for certificates to be used by various roadway systems.Certificate data 280 may be maintained identifying the issuance ofspecific certificates to specific roadway systems. Certificate data 280may also include information describing issues and reported misbehaviorassociated with specific certificates. For instance, roadway systems(e.g., 205) equipped with misbehavior detection logic (e.g., 215) mayidentify misbehavior involving messages associated with a particularcertificate and send misbehavior reports (e.g., 285) to correspondingauthorities (e.g., an authority governing misbehavior authority system250) to report these incidents to the certificate authorities. In someimplementations, a certificate manager 270 may include a trust engine275 to determine compliance (by certificate holders) to various rulesand policies associated with the certificate. Such policies may be thebasis of algorithms applied by the trust engine 275 to determine whethertrust in a particular certificate should be revoked, for instance, basedon repeated misbehaviors reported by various roadway systems throughcorresponding misbehavior reports 285. In some implementations, a trustengine 275 may be utilized to determined that a particular certificatehas been compromised by a malicious actor (based on repeated misbehaviorreports) and the certificate manager may revoke or cancel thecertificate based on the determined malfeasance. When revoked, theroadway system that holds the certificate may be unable to engage intrusted communications with other roadway systems (without having avalid certificate), thereby causing a compromised roadway system to beisolated and universally ignored (e.g., until trust can be reestablishedand a new certificate issued), among other examples.

Turning to FIG. 3, a simplified block diagram 300 is shown illustratingexample levels of autonomous driving, which may be supported in variousvehicles (e.g., by their corresponding in-vehicle computing systems).For instance, a range of levels may be defined (e.g., L0-L5 (405-435)),with level 5 (L5) corresponding to vehicles with the highest level ofautonomous driving functionality (e.g., full automation), and level 0(L0) corresponding the lowest level of autonomous driving functionality(e.g., no automation). For instance, an L5 vehicle (e.g., 335) maypossess a fully-autonomous computing system capable of providingautonomous driving performance in every driving scenario equal to orbetter than would be provided by a human driver, including in extremeroad conditions and weather. An L4 vehicle (e.g., 330) may also beconsidered fully-autonomous and capable of autonomously performingsafety-critical driving functions and effectively monitoring roadwayconditions throughout an entire trip from a starting location to adestination. L4 vehicles may differ from L5 vehicles, in that an L4'sautonomous capabilities are defined within the limits of the vehicle's“operational design domain,” which may not include all drivingscenarios. L3 vehicles (e.g., 320) provide autonomous drivingfunctionality to completely shift safety-critical functions to thevehicle in a set of specific traffic and environment conditions, butwhich still expect the engagement and availability of human drivers tohandle driving in all other scenarios. Accordingly, L3 vehicles mayprovide handover protocols to orchestrate the transfer of control from ahuman driver to the autonomous driving stack and back. L2 vehicles(e.g., 315) provide driver assistance functionality, which allow thedriver to occasionally disengage from physically operating the vehicle,such that both the hands and feet of the driver may disengageperiodically from the physical controls of the vehicle. L1 vehicles(e.g., 310) provide driver assistance of one or more specific functions(e.g., steering, braking, etc.), but still require constant drivercontrol of most functions of the vehicle. L0 vehicles may be considerednot autonomous—the human driver controls all of the drivingfunctionality of the vehicle (although such vehicles may nonethelessparticipate passively within autonomous driving environments, such as byproviding sensor data to higher level vehicles, using sensor data toenhance GPS and infotainment services within the vehicle, etc.). In someimplementations, a single vehicle may support operation at multipleautonomous driving levels. For instance, a driver may control and selectwhich supported level of autonomy is used during a given trip (e.g., L4or a lower level). In other cases, a vehicle may autonomously togglebetween levels, for instance, based on conditions affecting the roadwayor the vehicle's autonomous driving system. For example, in response todetecting that one or more sensors have been compromised, an L5 or L4vehicle may shift to a lower mode (e.g., L2 or lower) to involve a humanpassenger in light of the sensor issue, among other examples.

FIG. 4 is a simplified block diagram 400 illustrating an exampleautonomous driving flow which may be implemented in some autonomousdriving systems. For instance, an autonomous driving flow implemented inan autonomous (or semi-autonomous) vehicle may include a sensing andperception stage 405, a planning and decision stage 410, and a controland action phase 415. During a sensing and perception stage 405 data isgenerated by various sensors and collected for use by the autonomousdriving system. Data collection, in some instances, may include datafiltering and receiving sensor from external sources. This stage mayalso include sensor fusion operations and object recognition and otherperception tasks, such as localization, performed using one or moremachine learning models. A planning and decision stage 410 may utilizethe sensor data and results of various perception operations to makeprobabilistic predictions of the roadway(s) ahead and determine a realtime path plan based on these predictions. A planning and decision stage410 may additionally include making decisions relating to the path planin reaction to the detection of obstacles and other events to decide onwhether and what action to take to safely navigate the determined pathin light of these events. Based on the path plan and decisions of theplanning and decision stage 410, a control and action stage 415 mayconvert these determinations into actions, through actuators tomanipulate driving controls including steering, acceleration, andbraking, as well as secondary controls, such as turn signals, sensorcleaners, windshield wipers, headlights, etc. Accordingly, asillustrated in FIG. 5, the general function of an automated drivingsystem 505 may utilize the inputs of a one or more sensors devices 225(e.g., multiple sensors of multiple different types) and process theseinputs to make a determination for the automated driving of a vehicle.To realize the performance of the automated driving (e.g., acceleration,steering, braking, signaling, etc.), the automated driving system 505may generate one or more output signals to implement the determiningautomated driving actions and send these signals to one or more drivingcontrols, or more generally “actuators” 510, utilized to cause thecorresponding vehicle to perform these driving actions.

FIG. 6 is a simplified block diagram illustrating the exampleinteraction of components and logic used to implement an in-vehicleautomated driving system in accordance with one example implementation.For instance, a variety of sensors and logic may be provided which maygenerate data that may be used by the automated driving system, such asinertial measurement units (IMUS) 605, odometry logic 610, on-boardsensors 615, GPS sensors 616, map data 620, waypoint data and logic(e.g., 625), cameras (e.g., 630), LIDAR sensors 631, short range radarsensors 633, long range radar sensors 634, forward-looking infrared(FLIR) sensor 632, among other example sensors. Additional informationmay be provided from sources external to the vehicle (e.g., through anetwork facilitating vehicle-to-everything (V2X) communications (e.g.,635)) or from the user of the vehicle (e.g., driving goals (e.g., 640)or other inputs provided by passengers within the vehicle (e.g., throughhuman-machine interfaces (e.g., 652)). Some of these inputs may beprovided to a perception engine 238, which may assess the informationincluded in sensor data generated by one or a combination of thevehicle's sensors (or even external (e.g., roadside) sensors) andperform object detection (e.g., to identify potential hazards and roadcharacteristics), classify the objects (e.g., to determine whether theyare hazards or not), and track objects (e.g., to determine and predictmovement of the objects and ascertain whether or when the objects shouldimpact the driving of the vehicle).

Other sensors and logic (e.g., 616, 620, 625, etc.) may be fed tolocalization and positioning logic (e.g., 240) of the automated drivingsystem to enable accurate and precise localization of the vehicle by theautomated driving system (e.g., to understand the geolocation of thevehicle, as well as its position relative to certain actual oranticipated hazards, etc.). Results of the perception engine 230 andlocalization engine 240 may be utilized together by path planning logic242 of the automated driving system, such that the vehicleself-navigates toward a desired outcome, while more immediately doing soin a safe manner. Driving behavior planning logic (e.g., 650) may alsobe provided in some implementations to consider driving goals (e.g.,system-level or user-customized goals) to deliver certain driving oruser comfort expectations (e.g., speed, comfort, traffic avoidance, tollroad avoidance, prioritization of scenic routes or routes that keep thevehicle within proximity of certain landmarks or amenities, etc.). Theoutput of the driving behavior planning module 650 may also be fed intoand be considered by a path planning engine 242 in determining the mostdesirable path for the vehicle.

A path planning engine 242 may decide on the path to be taken by avehicle, with a motion planning engine 655 tasked with determining “how”to realize this path (e.g., through the driving control logic (e.g.,662) of the vehicle. The driving control logic 662 may also consider thepresent state of the vehicle as determined using a vehicle stateestimation engine 660. The vehicle state estimation engine 660 maydetermine the present state of the vehicle (e.g., in which direction(s)it is currently moving, the speed is traveling, whether it isaccelerating or decelerating (e.g., braking), etc.), which may beconsidered in determining what driving functions of the vehicle toactuate and how to do so (e.g., using driving control logic 662). Forinstance, some of the sensors (e.g., 605, 610, 615, etc.) may beprovided as inputs to the vehicle state estimation engine 660 and stateinformation may be generated and provided to the driving control logic662, which may be considered, together with motion planning data (e.g.,from motion planning engine 655) to direct the various actuators of thevehicle to implement the desired path of travel accurately, safely, andcomfortably (e.g., by engaging steering controls (e.g., 665), throttle(e.g., 670), braking (e.g., 675), vehicle body controls (e.g., 680),etc.), among other examples.

To assess the performance of the automated driving system and itscollective components, in some implementations, one or more systemmanagement tools (e.g., 685) may also be provided. For instance, systemmanagement tools 685 may include logic to detect and log events andvarious data collected and/or generated by the automated driving system,for instance, to detect trends, enhance or train machine learning modelsused by the automated driving system, and identify and remedy potentialsafety issues or errors, among other examples. Indeed, in someimplementations, system management tools 685 may include safetysub-systems or companion tools, and may further include fault detectionand remediation tools, among other example tools and relatedfunctionality. In some implementations, system management tools 685 mayadditional include misbehavior detection logic, such as discussedherein. In some cases, logic utilized to implement the automated drivingsystem (e.g., perception engine 238, localization engine 240, vehiclestate estimation engine 660, sensor fusion logic, machine learninginference engines and machine learning models, etc.) may be utilized tosupport or at least partially implement system management tools of thevehicle, among other example uses.

Turning to FIG. 7, a simplified diagram 700 is shown illustratingvarious types of vehicles (e.g., 705, 710, 715, 720, 730, 735, etc.)within a roadway environment. Vehicle-to-vehicle communication may beenabled in a subset of these vehicles. For vehicles with V2Xcommunication capabilities, it may be beneficial to also implementmisbehavior detection logic as a safeguard against vulnerabilitiesintroduced through V2X (and V2V) communications. For instance, vehicletypes may include connected vehicles (CVs) (e.g., 705, 735) capable ofV2V communication, which can receive and act on data received fromnearby vehicle (e.g., acting by showing a warning to the driver; sharetheir pose, speed, etc. with neighboring vehicles, etc.). Connectedautonomous vehicles (CAVs) (e.g., 710, 720) are another type of vehiclethat includes perception capabilities given by some sensors (e.g.,LiDAR). CAVs can share the result of their perception to any other CV orCAV, that is, such vehicles can not only share their pose, speed, etc.,but also information on the environment as they perceive it. Unconnectedvehicles (UVs) (e.g., 715, 725) are vehicles incapable of V2Vcommunication. UVs are not active participants of the shared perceptionsystem, but their very existence is critical to the safety of the road.For instance, CAVs can perceive the existence and characteristics of UVswithin the environment and inform other CAVs and CVs via V2Vcommunication as part of the implementation of shared perception betweenthe vehicles in the environment.

Messaging between vehicles (e.g., 740, 745, 750, 755) may take a varietyof forms and utilize any one of various protocols and technologies,which may be developed, standardized, and deployed for use in drivingenvironments. For instance, messages may be sent between vehicles (aswell as between vehicles and roadside units) according to a regularfrequency or based on particular events. In one example, communicationsgenerated by a roadway system may be periodic and broadcast (e.g., CVand CAV send a message every 100 ms). For instance, a roadway system maysend messages at fixed time intervals to every other connected roadwaysystem (e.g., CVs, CAVs, RSUs) within range of the communication. Inthis manner, roadway systems may share information (e.g., safetymessages, misbehavior messages, etc.) with each of its one-hopneighbors. For instance, Basic Safety Messages or Cooperative AwarenessMessages (referred to collectively herein as “safety messages”) may bebroadcasted periodically by each vehicle, typically with a frequencybetween 10 Hz and 3 Hz. Safety messages may be consumed and fused by theintelligent perception/tracking module of the autonomous drivingpipeline (e.g., the ADAS pipeline). Further, through V2X communications,a shared perception system may be embodied, for instance, where bothconnected roadway systems share lists of objects, that is, observationson themselves and on observed obstacles (where the roadway system iscapable).

Turning to FIG. 8, a simplified diagram 800 is shown illustrating anexample of a malicious act, which may leverage V2X communications toincorrectly influence the control of a victim vehicle. For instance, anadversary may compromise a vehicle's roadway system (e.g., on-board unit(OBU)) or steal credentials from a legitimate vehicle to craft anddistribute V2X messages containing fake information. As a consequence,these messages may be accepted by receiving roadway systems as passingthe cryptographic verifications at the victim vehicle or infrastructurenode (e.g., RSU), and the fake information may be used in potentiallysafety-critical applications. This type of attack has the potential tocause serious consequences, such as where a compromised roadway system(e.g., an OBU infected by a malware) alters values of outgoing V2Xmessages. For instance, as shown in the example of FIG. 8, a compromisedvehicle 805 utilizes V2V (or V2X) messaging (e.g., 810) to attempt toinduce errors by victim connected vehicles (e.g., 815). For instance,the compromised vehicle's roadway system may broadcast fake informationto nearby vehicles, which in this case, may adversely affect a vehicle(e.g., 815) with compromised line of sight (e.g., due to the presence ofanother vehicle, such as semitruck 825). For instance, such fakeinformation (e.g., a falsified safety message) may include a descriptionof the false position, orientation, and speed, leading a receivingvehicle's 815 roadway system to incorrectly assume the compromisedvehicle is behaving in a way it is not (as represented by 820). This maycause the victim vehicle 815 to trigger safety-critical reactions (e.g.,in response a dangerous cut-through maneuver 820, as illustrated in theexample of FIG. 8).

As introduced above, an improved misbehavior detection system may beprovided and enable collaborative misbehavior detection and reporting toidentify and remediate instances where malicious actors compromise V2Xsafety messaging. In some implementations, equipped roadway system mayact to detect misbehaving actors and report instances of misbehavior,which may trigger the revocation of such misbehaving actors in connectedvehicle systems. In some implementations, one or more of the roadwaysystems (e.g., vehicles or RSUs) may utilize local onboard sensingcapabilities to detect an inconsistency in V2X information reported by agiven vehicle with regard to the measured properties of the supposedmatching vehicle (e.g., using line of sight sensing through camera,radar, and/or LiDAR sensors, or other verification tools). Theinconsistency, or anomaly, may then be reported to other nearby vehiclesand/or to a backend misbehavior authority system in the infrastructure,such as the one provided by the Security Credential Management System(SCMS) or other entities, using a Misbehavior Report (MBR) messagegenerated by the detecting roadway system. Such messages may contain thedata reported in the anomalous message, as well as the sensed propertiesof the vehicle by the reporting vehicle as evidence of inconsistency. Amisbehavior authority system may collect such misbehavior reporting andfuse related reports to make a decision on the revocation of thevehicle.

In traditional systems, as illustrated in diagram 900 a of FIG. 9,extensive attackable space is made available for exploitation bypotential malicious actors (e.g., to introduce a ghost car through falseV2X safety messages in a Sybil Attack. Traditional systems may focusentirely on whether the V2X message includes inconsistencies, which mayleave vehicles more vulnerable to such attacks. An improved on-boardsafety system, such as described herein, implementing a misbehaviordetection engine to detect wrongful V2X safety messages, may reduce thearea available to attacker to potentially inject a ghost vehicle,through the use of perception systems available in at least some of thevehicles, as shown in diagram 900 b. Indeed, the absence of a ghost carmay be detected by any one of the connected vehicles (or RSUs)possessing perception logic as soon as the alleged vehicle enters thesensing range (e.g., 905) of one of the vehicles (e.g., 910), allowingthe inconsistency between reality and information within the suspectmessage to be detected and reported.

As introduced above, in some implementations, misbehavior detectionsystems may utilize object tracking (e.g., embodied in track data) toidentify inconsistencies in received V2X safety messages. In oneexample, V2X safety messages may be periodically broadcasted (e.g., withperiodicity, for instance, of 10 Hz), and contain at least the sendervehicle's position, heading, speed, and size (e.g., width and length). Amessage z₁ may represent a V2X message carrying information on vehicle i(e.g., 1005), as represented in the simplified diagrams 1000 a-cillustrating the example of FIG. 10). In this example, the perspectiveof a vehicle j (e.g., 1010) observing vehicle i 1005 is discussed forthe sake of illustration. It should be appreciated that similar examplesexist, for instance, with vehicle i observing vehicle j, a roadside unitobserving either (or both) of vehicles i and j, among other vehicles andexamples. For example, an RSU may aggregate camera or LIDAR feeds frommultiple surveillance cameras or LIDARs to obtain a birds-eyes view ofthe area, and thereby conduct object tracking and inconsistencydetection using its roadway system, such as described in the examplesherein. Indeed, an example RSU may have the ability to aggregate manymore sensors (e.g., cameras or LIDARs) along a section of a road, or anintersection, with more degree of freedom in terms of placementpositions and hence potentially better range and field of view) that isdifficult to do for an individual vehicle. An infrastructure unit, suchas an RSU, may also have more powerful computational resources than anindividual vehicle, among other example features. Indeed, vehicles andRSUs (or other infrastructure units) may cooperate together to conductmisbehavior detection simultaneously and enhance the quality of theresults and achieve better redundancy and system robustness.

Within the context of the illustrative example of FIG. 10, a firstvehicle (e.g., vehicle i 1005) is in the “line-of-sight” of another,second vehicle (e.g., vehicle j 1010), if the second vehicle can sense(and thus track) the first vehicle using its own local sensors. On theother hand, a first vehicle is “V2X-only” with respect to the secondvehicle, if the second vehicle does not have a line of sight with thefirst vehicle, but can receive V2X messages from the first vehicle (oranother vehicle sending messages reporting the presence of the firstvehicle). In other words, a vehicle is V2X only with respect to anotherif the only source of information on that vehicle is through a V2Xmessage (e.g., safety message) describing attributes of the vehicle. Thecorollary is that if a first vehicle is in the line-of-sight of a secondvehicle, the first vehicle is not V2X only with respect to the secondvehicle.

In traditional systems, onboard sensors may realize a field of view, orreliable line-of-sight, that has a shorter range that V2Xcommunications. Indeed, in practice, full or partial occlusions ofon-board sensor materialize due to buildings, larger trucks, and curvyroads commonly found in the real world. Accordingly, the field of viewand range of sensors tend to be a subset of the V2X communication range.Further, over time a vehicle i 1005 may transition from V2X-only vehicleto a vehicle in line-of-sight and back again to V2X-only vehicle withregard to another vehicle j 1010 as it drives toward and then passesvehicle j, as illustrated in FIG. 10. For instance, at a time t=n,represented in diagram 1000 a, vehicle i 1005 may be outside theline-of-sight of vehicle j 1010 by virtue of the presence and positionof truck 1015. At time t=n+1, represented in diagram 1000 b, vehicle i1005 may have accelerated and passed the truck 1015, bringing vehicle i1005 into the line of sight of the sensors of vehicle j 1010. Thepositions of the vehicles 1005, 1010, 1015 may change dynamically aseach vehicle drives within the environment, which may result in vehiclei 1005 falling outside the line-of-sight of vehicle j 1010 again, asillustrated in diagram 1000 c, and so on.

In some implementations, vehicles may be capable of performing Detectionand Tracking of Moving Objects (DATMO) to track nearby vehicles thatthey have in their LOS, and to estimate their position, speed, headingand size. V2X messages (e.g., safety messages) may serves as anadditional source of information to use to track other vehicles overtime. Indeed, information obtained through V2X for a particular object(e.g., another vehicle) may be fused with the information obtained bydirect perception (e.g., using camera, LiDAR, or other systems togetherwith on-board perception logic). Information gleaned for an object fromV2X messages and/or perception may be recorded in object tracksmaintained in track data of a corresponding roadway system. The tracksmay describe a list of objects identified by the roadway system overtime through perception or V2X reporting. In one example, object tracksmay be implemented as three types of tracks: V2X-only Tracks (VT) thatmaintain V2X-only object data (e.g., BSMs) when the message describes anobject outside the line-of-sight of the system; LOS-only Track (LT) datathat tracks information generated for an object using the roadwaysystem's sensors and perception logic (but for which no V2X messageshave been received for the object (or where the roadway system does notsupport such messaging); and Mixed Track (MT) data for objects for whichboth information is obtained from a combination of both V2X messages andvehicle sensors. In other instances, tracks may be maintained for eachobject encountered by a roadway system (e.g., within a time window) andthe corresponding track data may identify and differentiate betweeninformation collected through sensors/perception and informationcollected from received V2X message, among other exampleimplementations.

Object tracks may be utilized by a misbehavior detection system todetect inconsistencies in subsequent V2X messages including informationdescribing the tracked object. For instance, a ghost vehicle may bedetected utilizing track data. For instance, vehicle i may be a GhostVehicle (GV) for a vehicle j, GV_(j) (i,t), if vehicle j detects aninconsistency between vehicle j's knowledge of vehicle i and vehicle j'ssurroundings. In one example, information reported within an example V2Xsafety message may place the location of the sending vehicle within anarea detected as free by the receiving vehicle's sensors (and recordedin corresponding track data). As a result, the roadway system of thereceiving vehicle may determine an anomaly or inconsistency within thecontent of the received message. The inconsistency (and roadway systemresponsible for the inconsistency) may then be tracked (e.g., using awatchlist) to determine repeat instances of the same (or different)inconsistencies in messages sent by the sender. Remedial actions may betaken, including messaging a misbehavior authority system, which maypotentially revoke the communication privileges of the source systembased on misbehavior report messages reporting the inconsistencies,among other example implementations.

Turning to the flow diagram 1100 of FIG. 11, a multi-tier misbehaviordetection approach is outlined. As introduced in FIG. 2, someimplementations of a roadway system may include misbehavior detectionengine capable of detecting anomalies or inconsistencies in V2X safetymessages. Further, V2X messaging can itself be leveraged to enablecollaborative misbehavior detection, prevention, and remediation. Forinstance, an example misbehavior detection system may include both localanomaly detection logic, which allows vehicles to autonomously detectanomalies (and thus misbehaviors) in the V2X data, and collaborativedetection logic, where vehicles report locally detected anomalies withinmisbehavior reports (MRs) broadcasted to their neighbors. Receivedmisbehavior reports may then bs used to make decisions locally on thepotential misbehavior of a sending vehicle.

The example of FIG. 11 is a diagram 1100 illustrating the flow of anexample misbehavior detection engine of a roadway system that includesmultiple stages of misbehavior detection. For instance, a V2X message1105 (e.g., safety message) may be received and be first processed by amessage analysis engine 220 providing message-level anomaly detection.The message analysis engine 220 may parse the message 1105 to detectanomalies in the objects reported in the message 1105 on a per-messagelevel, such as by performing simple range checks (e.g., accelerationoutside predefined boundaries, sudden appearance/teleporting of anobject, etc.) and identifying instances where the same object isredundantly identified, etc. The message 1105 may parsed to identify theindividual objects 1110 identified by the sender of the message 1105.The objects identified in messages received from remote senders may beaccumulated 1115 and held in a buffer (e.g., 1120), which continues toidentify, on a sender-by-sender basis, the objects (and objectattributes) reported by sending roadway systems (e.g., connectedvehicles and RSUs) in an environment. In some implementations, trackdata (e.g., VT, LT, MT tracks) may be used to buffer and track objectsidentified by various senders.

Continuing with the example of FIG. 11, track data (e.g., in 1120) maybe utilized by additional misbehavior detection stages, such as stagesfacilitated by a message prediction engine 222, and overlap detector224. For instance, a message prediction engine 222 may providemodel-level anomaly detection by detecting anomalies in the evolution ofthe information provided by any single sender over time. For instance, amodel can identify the predicted state of an object after an amount oftime has elapsed (e.g., corresponding to the time between messages)based on information contained in the immediately preceding messagessent by the sender and describing the object and determine anomaliesbased on the residual between the predicted state and the newinformation reported for the object in the message (e.g., 1105). Anoverlap detector 224 may look at anomalies at a higher level amongobjects and search for overlaps that may occur, which may indicate thepresence of a misbehaving entity fabricating a ghost vehicle (which“overlaps” with another real vehicle). Such overlaps can be detectedfrom the tracking information maintained and accumulated by receivingroadway system. In some implementations, a roadway system may haveaccess to local sensors and sensor data that may be provided as inputsto a perception system that enables detection, recognition, and trackingof specific objects in a driving environment. The locally detectedobjects may be likewise stored and tracked (e.g., in data store 1125).An overlap detector 224 may, in such implementation, use informationdescribing both remotely detected objects and locally perceived objectsto perform system-level anomaly detection.

Remotely detected and reported objects may be further processed at thereceiving roadway system to align the object reports on the basis oftime (at 1130), cluster the object reports 1135 (e.g., to matchinformation from messages originating from different senders thatdescribe the same object), and create time-aligned object data for eachreported object through cluster merging 1140. The clustered objectinformation identified from V2X messages (e.g., 1105) may betime-aligned with corresponding information describing objects detectedlocally by the roadway system's perception logic (at 1145).

In some implementations, local perception logic of a roadway system maybe effectively combined with those of other, neighboring roadway systemsto implement a shared perception system where connected autonomousvehicles and even connected non-autonomous vehicles may share a list ofN+1 perceived objects, where N objects are the objects perceived by thevehicle (e.g., if capable—in the case of non-autonomous, non-perceptivevehicles N=0), with 1 object describing the sender vehicle itself. Forinstance, a roadway system may maintain a list of objects S, which isupdated with incoming V2X information. After a new message (e.g., 1105)is received from a sending vehicle, a receiving vehicle v_(r) decodes1108 the list of objects 1110 contained in it, and accumulates 1115 thereported objects for tracking (e.g., using a Kalman Filter). Messagesmay be received at a particular cadence, with the receiving roadwaysystem fetching (e.g., at 1108) each of the latest objects (or datadescribing the detected objects in a driving environment) from all theneighboring senders, and aligns 1130 them to the latest timestamp (e.g.,using a same or similar time reference, such as GPS time). In somecases, time alignment 1130 may be accomplished according to a model(e.g., a constant velocity model), which may be used to predict thestate of objects in the environment, for which the longitudinal positionof an object at time t+Δt is given by x_(t+Δt)=x_(t)+{dot over(x)}_(t)Δt. After alignment 1130, objects from multiple senders areclustered 1135 based on a distance metric (e.g., the Mahalanobisdistance, which indicated the distance between objects if they arerepresented as Multivariate Gaussian variables). Clusters are thenmerged 1140 to produce a list of merged remote objects. Such list isthen time-aligned 1145 with the current system objects (e.g., at 1125)detected using the receiving roadway system's sensors and perceptionlogic, for instance, using an association algorithm (e.g., GlobalNearest Neighbor), which maps 1150, if possible, remote objects (at1120) to local objects (at 1125) (e.g., matching objects from the twosets that likely describe the same physical entity). Matching pairs ofobjects may be merged or fused 1155 together, while unmatched remoteobjects are added as new system objects for tracking. In someimplementations, objects added to track data that remain un-updated fora long time may be removed according to some management policy, amongother example features. In this manner, remotely identified objects maybe associated with the locally perceived objects to develop objectrecords (e.g., tracks) that incorporate both the remotely identifiedobject information (from potentially multiple roadway systems) with thelocally perceived object information. Such collective object informationmay be utilized by a perception analysis engine 226 to performperception-level anomaly detection. For instance, the perceptionanalysis engine 226 may detect inconsistencies between the space asperceived by the vehicle sensors, and those described in V2X messages(e.g., 1105). For example, a ghost vehicle may be present in an areathat sensors detected as free, thus constituting an evidentinconsistency.

At any one (and potential multiple) of the stages (corresponding to 220,222, 224, 226), an anomaly may be detected and reported 1160 amisbehavior decision making engine 1165. In some cases, based on theseverity of an anomaly or based on repeated anomalies involving aparticular sender or object, the roadway system may generate misbehaviorreport message to send to other nearby roadway systems (e.g., othervehicles or RSUs) and/or a backend misbehavior authority system, amongother examples. As shown in the example of FIG. 11, received V2Xmessages (e.g., 1105) may contain misbehavior reports of their own,which may also be considered and acted upon by misbehavior decisionmaking engine 1160 to cause reports or remedial action to be initiatedby the roadway system. Misbehavior reports may form the basis forcollaborative misbehavior detection and remediation. A misbehaviordecision making engine (e.g., 1160) uses local anomaly detection asprimary input. If no alarms were triggered, there is still a possibilitythat an attacker is mounting an attack (e.g., a ghost vehicle attackwhere the ghost vehicle is outside the field of view of receivingroadway system' sensors). Accordingly, a misbehavior detection engine ofthe roadway system can also consider misbehavior reports sent by peersto detect and react misbehaviors. For instance, if the number ofmisbehavior reports sent for a potential malicious sender is greaterthan the redundancy of the object (e.g., the number of independentsources confirming the existence of that vehicle), the receiving roadwaysystem may consider the input from the particular sending system to bemalicious and discard all the object information originating from thissender (e.g., a purge associated information from track data maintainedat the receiving system. In some cases, evidence of misbehavior may bereceived (e.g., in a received misbehavior report from a remote roadwaysystem), but the evidence is insufficient to definitively determine thata particular sender should not be trusted, the receiving roadway systemmay continue to use the received V2X object information (e.g., to detecta forward collision and raise a warning). In some cases, a report ofmisbehavior involving a peer system may cause the receiving roadwaysystem to watchlist this system (e.g., to determine if repeatedmisbehaviors are detected/reported) and may even treat information from“watchlisted” systems with more caution (e.g., at a reduced trust level)than information from a sender not on the receiving system's watchlist,among other examples.

In some implementations, an end-to-end misbehavior detection andmanagement system may be implemented using roadway systems (on vehiclesand RSUs) and one or more backend misbehavior authority systems. Anend-to-end misbehavior management may include phases of (1) localinconsistency detection and reporting, and (2) reports processing anddecision-making actuation. In the local inconsistency detection andreporting phase, a vehicle or a local infrastructure entity detects aninconsistency between the maintained object tracks and one or morereceived V2X messages. Such inconsistencies may be determined by roadwaysystems implemented on vehicles, RSUs, drone, among other examples.

In some implementations, the roadway system of a receiving vehicle mayprocess an incoming BSM. The roadway system may maintain a blacklist ofdetected misbehaving senders, against which it matches every incomingBSM before signature verification. If the sender ID is not in theblacklist, the receiver checks whether the ID corresponds to one of theobject tracks maintained by the roadway system. For instance, thereceiving roadway system may check if the safety message references anobject for which there is a known MT (e.g., a known V2X+LOS track). Ifthis is the case, the receiving roadway system computes a statisticaldistance d=D(,) between the BSM data and the track, which gives ameasure of the “divergence” of the V2X data with regard to the expectedand measured value (e.g., based on perception of the same object by theroadway system (e.g., using the corresponding vehicle's sensors)). Insome implementations, the receiving roadway system compares thecalculated divergence, or value d with a predefined threshold T. Forinstance, if d>T, and this is the first time the BSM deviates from theMT, the receiver computes a time tolerance δt, corresponding to theamount of time (or number of subsequent messages) a given sender's BSMsmay remain “anomalous” (e.g., outside the acceptable statisticaldistance from the ground truth determined using sensors and perceptionlogic of the roadway system). If a set of BSMs from a sender areconsistently anomalous over a defined number of messages or amount oftime, the roadway system may determine that these anomalies constitutemisbehavior on the part of the sender. Accordingly, the roadway systemmay cause a misbehavior report message to be sent to other roadwaysystems associated with other vehicles, RSUs, etc. Additionally, larger“deviations” of the BSM data from existing track values may lead to moreimmediate and/or severe consequences on the safety of other vehicles. Insome implementations, the larger the deviation d, the smaller the atused to determine misbehavior, among other examples.

As an illustration, FIG. 12 illustrates two scenarios (in diagrams 1200a, 1200 b) to show, by way of comparison, examples of a difference inthe deviation of a message's description of an object versus what isobserved by a particular vehicle (e.g., 110) using the vehicle's 110perception system. In this example, the message may be received over awireless communication channel (e.g., as a V2X BSM) from the roadwaysystem of another vehicle (e.g., 115). The particular vehicle 110 mayprocess the message (using its roadway system) to determine that one ormore of the objects identified and described in the message does not fitwhat the vehicle 110 observes using its own sensors. For instance, themessage may describe the vehicle 115 itself. In the example of diagram1200 a, a message 1205 from vehicle 115 may indicate that the vehicle115 is moving slightly outside the lane 1210. However, the recipientvehicle 110 may perceive that the vehicle 115 is instead maintaining itscourse within the lane 1210. Accordingly, the roadway system of vehicle110 may indicate an inconsistency or anomaly. In this example, theroadway system of vehicle 110 may regard the inconsistency as arelatively minor divergence from what is observed or expected and,rather than blacklisting vehicle 115 or sending a misbehavior message,place the vehicle's 115 roadway system on a watchlist (e.g., identifyingthe vehicle 115 by a corresponding certificate used in V2Xcommunication). For instance, the recipient roadway system may track thetime or number of messages which pass since a BSM received from vehicle115 was first classified as anomalous and adds the sender to awatchlist.

Both the first time, and subsequent times where the calculatedstatistical divergence of the anomaly is d>T, if the elapsed time isgreater than at, the recipient roadway system may drop the BSM (andrelated data tracked in associated track data), issue an MBR message,and add the sender system's identifier to the blacklist. In someexamples, if a received message is detected as including an anomaly butwith divergence of d<T, then the recipient roadway system may simplydiscard/ignore the message, while continuing to track the suspect systemfor subsequent anomalous MBRs. In this example, when an anomalousmessage is received for a particular object reported by a particularroadway system, the related track may be changed (where applicable) frommixed (MT) to line-of-sight only (LT) (as the most current trackinginformation is from the recipient roadway system's perception systemonly), among other examples. If a suspect roadway system has been addedto a watchlist based on a previous anomalous message, but subsequentmessages do not contain inconsistencies or anomalies (e.g., where theinitial anomaly was an outlier (e.g., due to a temporary inaccuracy ofits localization algorithm), the suspect roadway system may be removedfrom the watchlist, and its BSM data may be fused with related MT trackdata, among other examples.

Continuing with the example of FIG. 12, in the example of diagram 1200b, the message (e.g., 1215) received from vehicle 115 may indicatemovement of the vehicle 115 from a different lane (e.g., 1220)indicating an uncharacteristic exist from the lane 1220. However, theperception system of the receiving roadway system (of vehicle 110) mayagain indicate that the subject object (vehicle 115) is actuallyproceeding forward within lane 1210. Based on a comparison of therecipient roadway system's perception of the vehicle 115 (and itsposition and movement) an inconsistency may be determined within thecontents of message 1215. Additionally, a divergence value may bedetermined based on the degree of divergence between the information inthe message 1215 and observed ground truth. In this example, rather thanbeing a divergence d<T (e.g., as in the scenario illustrated in diagram1200 a), the recipient roadway system may determine a divergence d>Tand, rather than watch-listing the sender 115, may immediately generateand send a misbehavior report, place the sender on a local blacklist,among other remedial actions, based on the severity of the divergence,among other examples (such as the example of FIG. 13, where a message(e.g., 1305) to vehicle 110 from a malicious actor 1310 represents thepresence of a ghost vehicle (e.g., 1315) within a free space 1320 (e.g.,between vehicles 110, 115, 1325) detected by the vehicle's 110 roadwaysystem, etc.).

FIGS. 14A-14B show a flow diagram 1400 (continued from FIG. 14A to FIG.14B) illustrating the example processing of an example basic safetymessage from a sending roadway system upon receipt 1402 of the messageat a recipient roadway system. In one example, the recipient roadwaysystem (or “recipient”, for simplicity in describing this example) maydetermine 1404 if an identifier (e.g., certificate ID, vehicle ID, etc.)is present on a local copy of a blacklist (e.g., a local blacklist orglobal blacklist). If the identifier of the sending roadway system (orsimply “sender” within this example) is blacklisted, the recipient maysimply drop 1406 the message without further processing. If, however,the sender is not blacklisted, the recipient may further determine 1408whether an identifier of the sender is included in mixed track data(e.g., has a corresponding MT ID). If not, the recipient may furtherdetermine 1410 whether the sender's ID corresponds to any V2X tracks(e.g., VT IDs). If the sender ID corresponds to a known VT, theinformation within the BSM may be fused 1411 with a corresponding VT. Ifthe sender ID does not correspond to existing MTs (or VTs), the receivermay perform 1412 range checks (e.g., comparing the distance calculatedwith BSM's location with the maximum wireless range, or checking whetherthe BSM places the vehicle in a drivable space) to determine 1414validity of the message (at the message level). For instance, if themessage fails the range check(s), the message may be discarded 1416(e.g., and the sender ID added 1418 to a local blacklist).

If range checking 1414 is passed, the BSM data may be matched 1420 onthe current set of LTs in the system tracks list, using some statisticaldistance measure (e.g., the Mahalanobis distance or other measure ofstatistical divergence between what has been perceived by therecipient). If the BSM matches (at 1422) an existing LT (e.g., a trackfor an object detected using receiver's line-of-sight (LOS) sensors),the track's ID is updated 1424 with sender's ID, and the BSM data isfused 1426 with the existing track (e.g., the LT data is labelled asMT). If the BSM does not match (at 1422) an existing LT, a consistencycheck is run to see of the information of the BSM matches 1428perception results of the recipient based on the recipient's rangesensor data to determine 1430 whether the content of the BSM conflictswith the recipient's range sensors or not (e.g., where a conflict mayindicate a Ghost Vehicle (GV). If such an anomaly is detected at 1430(e.g., BSM data places a vehicle on a free space such as in the exampleof FIG. 13), the message may be discarded 1416, sender ID is added 1418to a local blacklist, and a misbehavior report message 1432 may begenerated and sent to other systems (e.g., neighboring vehicles, drones,RSUs, etc. or remote systems, such as a misbehavior authority system).On the other hand, if the information within the BSM matches (at 1434)(e.g., within a tolerance) the information determined by the recipient'ssensors and perception system, a new MT record may be generated 1436corresponding to the BSM and information of the BSM may be documented inthe record. If not, a new VT may be similarly generated 1438 andpopulated with information from the BSM (e.g., based on the BSMbelonging to a non-line-of-sight area for the receiver). In someimplementations, VTs may be periodically matched against LTs, andpossibly fused into MTs.

If a sender of a BSM is determined to be a known MT ID (and have acorresponding MT), the BSM may be time-aligned 1440 with the matching MTdata and a divergence d may be determined for the information within theBSM (e.g., as compared with preceding information for the same object(s)described in the BSM) and compared 1442 against a threshold T. In thisexample, if d<T, it may be determined that the divergence may beignored, causing the recipient to determine 1444 whether the ID of thesender is being tracked in a watchlist (e.g., based on a previouslydetected anomaly in a preceding message from the sender). If the senderis in a watchlist, it may be removed 1446 from the watchlist, prior tofusing 1426 the information within the BSM within the corresponding MT.

On the other hand, if d is not less than the threshold T (at 1442) thesender may be watched, which may include determining 1448 whether thisis the first (e.g., within a time period) divergence in a messagereceived from the sender at the recipient. If so, a time tolerance maybe computed 1450 based on the divergence (e.g., with longer timetolerances being computed for less serious divergences), the ID of thesender may be added 1452 to a watchlist, and the time of the occurrenceof the anomalous BSM may be recorded 1454 in the watchlist (e.g., withthe time tolerance to determine the expiration of the time tolerancewindow). If the anomalous BSM is not the first occurrence, a timetolerance and time of initial anomalous message may already bedetermined, allowing a time since the first divergence to be calculated1456 by the recipient. If the elapsed time is greater than the thresholdset by the time tolerance (at 1458), then given that the received BSMcontinues to include inconsistencies or anomalies (e.g., even when thedistance/divergence of the latest BSM is less than that of one of thepreceding divergent BSMs from the sender), the latest BSM may be dropped1416, leading to the sending of a misbehavior report message (e.g.,1432) and/or blacklisting (e.g., 1418) of the sender. If the calculatedtime threshold has not elapsed, the sender may remain on the watchlist(e.g., where a subsequent BSM from the sender may potentially result inthe determination of a misbehavior) and the MT associated with thesender may be converted 1460 to an LT record (based on the flaws in thisV2X BSM) and the BSM may be ignored (e.g., 1406) with no furtherremedial action 1462.

In some implementations of a misbehavior detection system of a roadwaysystem, as result of the DATMO process, each roadway system maintainsover time the following state for each tracked object (or track), attime t:

o _(i)(t)=

(μ_(i)(t)=[x(t), y(t), {dot over (x)}(t), {dot over (y)}₍ t), w (t),l(t)], P _(i)(t)), i=1, 2, . . . , M,

where

indicates a (multivariate) normal distribution with mean μ andcovariance matrix P; x, y is the position of the vehicle's center in 2Dcoordinates system, {dot over (x)}, {dot over (y)} represent thevelocity of the vehicle along the two axes, and w, l are width andlength of the vehicle. Additionally, each track may have an associatedID id_(i) and may be labelled as a VT, MT or LT. Accordingly, in oneexample, a (signed) BSM message m_(i)(t′) sent at time t′ by a vehicle iis defined as follows:

m _(i)(t′)={id _(i) , t′, z _(i)(t′)=[x _(i)(t′), y _(i)(t′), {dot over(x)}_(i)(t′), {dot over (y)}_(i)(t′), w _(i)(t′), l _(i)(t′)]},

A vehicle j that receives m_(i)(t′) at time t, once verifying that id₁does not appear in j's blacklist, may attempt to match z_(i)(t′) againsteach o_(k)(t′), k=1, 2, . . . , M, by ID. In some instances, id_(i)matches an existing MT. Let o_(k)(t′) be such a track. The track isfirst aligned in time with the BSM data (e.g., using a Constant Velocitymodel to predict the track's state forward in time). Then, the receiverestimates the distance of z₁(r) from o_(k)(t′) based on, for example,the Mahalanobis distance. If the distance is lower than a pre-definedthreshold T, z_(i)(t′) is fused with o_(k)(t′) (e.g., using a KalmanFilter (KF)). The Mahalanobis distance may distributes according to a_(X) ² distribution, and thus the threshold may be derived depending onthe degrees of freedom, and the intended confidence interval. Forinstance, if the divergence/distance d is greater than T, and if this isthe first time this happens, j determines the amount of time steps δt towait before reporting the sender as misbehaving. δt is later used todetermine, given n subsequent time periods of inconsistent V2X data,whether the inconsistency shall be reported to a misbehavior authorityusing a misbehavior report (e.g., if n>δt).

In other instances, id_(i) does not match an existing MT. Accordingly,in this case, if id_(i) matches an existing VT, the track is fused andthe algorithm terminates, otherwise, z_(i)(t′) is checked to be withinthe radio range of j: if not, the message is dropped and the senderreported, otherwise j searches for a matching LT by calculating thedistance of z_(i)(t′) from every LT. In case of a match, z_(i)(t′) maybe fused with the matching LT, which is then flagged as MT, and itsidentifier is updated with id_(i). Otherwise, z_(i)(t′) is matchedagainst range sensors output (e.g., against a processed point-cloud orOccupancy Grid Map calculated by j). As an example, if the bounding boxdescribed by z_(i)(t′) overlaps significantly with an area determinedfree by the Occupancy Grid Mapping algorithm (e.g., if the report placesthe sender in a space that was detected as free by the receiver's rangesensors) an anomaly flag may be raised and j may issue an MBR. If thereis no conflict between range sensors and V2X data, a new VT may beinitiated.

In some implementations, id_(i) does not match an existing MT.Accordingly, in this case, if id_(i) matches an existing VT, the trackis fused and the algorithm terminates, otherwise, z_(i)(t′) is checkedto be within the radio range of j: if not, the message is dropped andthe sender reported, otherwise j searches for a matching LT bycalculating the distance of z_(i)(t′) from every LT. In case of a match,z_(i)(t′) may be fused with the matching LT, which is then flagged asMT, and its identifier is updated with id_(i). Otherwise, z_(i)(t′) ismatched against range sensors output (e.g., against a processedpoint-cloud or Occupancy Grid Map calculated by j). As an example, ifthe bounding box described by z_(i)(t′) overlaps significantly with anarea determined free by the Occupancy Grid Mapping algorithm (e.g., ifthe report places the sender in a space that was detected as free by thereceiver's range sensors) an anomaly flag may be raised and j may issuean MBR. If there is no conflict between range sensors and V2X data, anew VT may be initiated.

Regarding the example above, in implementations, other representationsmay be utilized in the data exchanged by vehicles. For instance, avehicle may send its location (x,y), heading (0), and forward speed (s).The difference between a “measurement” representation and the “state”representation may be handled by filters such as the KF, which operatesusing a transition matrix H (linear transformation) or function h(non-linear transformation) that converts state to measurementrepresentation, among other examples. In some cases, nonlinearity mayrequire the use of filters that can handle nonlinearity, such as theExtended KF (EKF) or Unscented KF (UKF), among other examples.

In some implementations, during a reports processing, decision making,and actuation phase, in response to detecting an act of misbehaviorinvolving V2V or V2X messaging by a particular roadway system (e.g., ona vehicle, roadside unit, drone, etc.), another roadway system may senda corresponding misbehavior report message (MRM). In someimplementations, such misbehavior report messages may be sent orforwarded to backend processing by a misbehavior authority, who may, insome cases, have discretion or authority to invalidate, downgrade, orotherwise change an offending system's communication credentials. Insome implementations, different misbehavior report messages may be usedfor reporting immediately detected misbehavior to neighboring systemsthan used for reporting more detailed accounts of the misbehavior (anddata used by the local roadway system in determining associationmisbehavior) to a backend misbehavior authority, given the differentuses of the information in these contexts. For instance, localmisbehavior report messages sent to neighboring vehicle and RSU roadwaysystems may be utilized to cause these vehicles to add a correspondingsystem ID to a watchlist or blacklist for immediate use by the receivingroadway system. On the other hand, a misbehavior authority may compilemisbehavior report messages over a period of time received in varioustime windows from various roadway systems and more carefully analyze thecontents of misbehavior report messages relating to alleged misbehaviorby a particular roadway system to determine whether to revoke anautonomous vehicle's communication credentials (e.g., SCMS certificate)should be revoked.

In one example, misbehavior report messages sent by vehicle and RSUroadway systems to a backend misbehavior authority system may includeinformation to allow the misbehavior authority to judge the accuracy andbackground behind alleged, detected misbehavior. For instance, suchmisbehavior report messages may include reporter position (e.g., in GNSScoordinates), a copy of the message(s) (e.g., basic safety message)representing the act of misbehavior, data collected (e.g., by thereporting system's local sensors) as evidence of the inconsistency(e.g., in the form of an objects list, or Occupancy Grid Map, etc.),among other example information. A misbehavior report message may besigned with the reporting roadway system's credentials to ensure theauthenticity of the misbehavior report message (e.g., and mitigateagainst malicious use of the misbehavior reporting regime), among otherexample features.

In one example, when backend misbehavior authority system receives a setof misbehavior report messages, the misbehavior authority may determinewhether and what remedial actions to initiate. In some implementations,a backend misbehavior authority system may weight different reportmessages differently when assessing the trustworthiness of the roadwaysystem being complained of in the misbehavior report messages. Forinstance, the trust or quality of the reporting roadway system'spoint-of-view may be assessed, among other characteristics. In oneimplementation, upon receiving and identifying a related set ofmisbehavior report messages (e.g., identifying the same roadway system(e.g., by certificate ID)), the misbehavior authority system may computea representation of the field of view of every one of the reportingvehicles with regard to the target vehicle (or the vehicle to examinefor potential certificate revocation). For instance, FIG. 15 is a set ofdiagrams 1500 a-c illustrating a driving environment at the time of two(or more) reported misbehaviors, measured and reported by respectiveroadway systems on vehicles 105 and 110 involving misbehavior by aparticular roadway system on another vehicle 115. Diagram 1500agenerally illustrates the driving environment, where vehicle 115 sharesthe road with other road actors, including vehicles 105, 110, 1505,1510, etc.

As shown in the example of FIG. 15, some vehicles may have been in abetter position both physically in terms of the sensors available on thevehicle to provide a field of view representation that better capturesthe behaviors of the target system (e.g., of vehicle 115). Diagram 1500b represents that field of view captured by vehicle 105, which may havesent a first misbehavior report to the misbehavior authority system,while diagram 1500 c represents the field of view captured by anothervehicle 110 also reporting misbehavior of vehicle 115, among otherillustrative examples. Representations of the respective field of viewsof the reporting vehicles may be included or derived (e.g., by themisbehavior authority system) from the misbehavior report message(s)received from the reporting vehicles. The field of view (FOV) models maybe used by the MA to estimate the number of potential witnesses and,from it, derive a minimum number of reports nr_(min) to obtain in orderto make a decision for the target roadway system 115. Further, usinginformation in the misbehavior report messages, the misbehaviorauthority system may calculate a weight w that describes the reliabilityof the report, for instance, based on the estimated FOV model. As anexample, weights may be computed according to the persistenceprobability p_(p) in a multi-object tracking application. For instance,in the example of FIG. 15, different weights may be assigned to therespective reports from vehicles 105 and 110. In one example, given thatvehicle 110 has a clearer view of the target vehicle 115, themisbehavior report message originating from vehicle 110 may beconsidered more reliable than the one from vehicle 105 and may beafforded a relatively higher weight. In some implementations,misbehavior authority system may compute the sum of all the weightsdetermined for reports received for a particular target system. Thesevalues may be used to determine the weight that should be afforded toanyone of the misbehavior reports or the combined gravity of acollection of reports. In some implementations, a single misbehaviorreport may trigger remedial action by the misbehavior authority of thedetermined weight of the misbehavior report is above a threshold value.In some instances, revocation of the target system's certificate may bethe remedy determined by the misbehavior authority system. Lesseractions may also be triggered, such as reducing a level of trustassociated with the target system, placing the certificate of the targetsystem in a watchlist for further assessment, among other exampleremedies. In one embodiment the revocation may be carried out using aCertificate Revocation List (CRL) including the target vehicle ID in it,among other example revocation techniques.

FIG. 16 is a simplified block diagram 1600 illustrating an example of acertificate management architecture, in which a misbehavior authoritysystem (e.g., 250) may be a part. For instance, a variety of actors andsystems may be involved in implementing the certificate managementsystem 1605 (including policy 1610 and technical 1615 actors andsub-systems). Some actors may be intrinsically central to thecertificate management of various roadway systems (e.g., 1620, includingthose of vehicles (on-board equipment (OBEs) implementations), roadsideunits (e.g., roadside equipment (RSEs)), mobile devices (e.g.,aftermarket safety devices (ASDs)), etc. A policy generator 1625 maypush policies for use and management of communication certificates toall members of the architecture 1605, including certification services1622, enrollment certificate authorities (CA) 1624, registrationauthorities 1626, etc. In some implementations, certificates may behierarchical, with root certificates (managed by root CAs (e.g., 1630),intermediate certificates (managed by intermediate CAs (e.g., 1635), andso on. Linkage authorities (e.g., 1640) may be utilized together withregistration authority entities (e.g., 1626) and misbehavior authoritysystems managing misbehavior reports (e.g., system 250). As noted above,a CRL may be utilized to establish and enforce revocations ofcertificates. For instance, an example misbehavior authority system 250may include a CRL generator 1650 to generate and update CRL(s), whichmay be pushed to one or more CRL stores (e.g., 1655) for access byroadway systems in determining whether to trust and engage incommunication with another roadway system within a driving environment,among other example components and implementations.

While much of the above discussion has focused on in-vehicle androadside systems monitoring road safety events and apply vehicle safetystandards to incidents involving at least partially autonomous roadvehicles, it should be appreciated that the principles discussed hereinmay equally apply in other environments, where machines, designed tomove autonomously, may be potentially targeted for exploitation usingcompromised safety messages. For instance, similar solutions and systemsmay be derived based on the principles above for machines includingaerial vehicles, watercraft, unmanned drones, industrial robots,personal robots, among other examples.

FIG. 17 is a simplified flow diagram 1700 illustrating an exampletechnique for detecting communication-base misbehavior by a roadwaysystem within an autonomous driving environment. For instance, a safetymessage may be received 1705 at a first roadway system (e.g., anautonomous vehicle system, drone, roadside unit, etc.) from anotherroadway system (e.g., of another autonomous vehicle, roadside unit,etc.). The safety message may describe one or more physical objects thatare reported (by the second roadway system) as being present within thedriving environment. The first roadway system may determine 1710characteristics of the driving environment, including characteristics ofthe object (including whether the object is or is not present within thedriving environment, its position within the environment, its size,whether and how the object is moving, etc.). The characteristics may bedetermined 1710 based on sensor data generated by sensors local to thefirst roadway system, as well as machine perception logic of the firstroadway system. Additional characteristics may also be determined forthe physical object based on other safety messages from other roadwaysystems also describing the object. The characteristics may be compared1715 with the description of the object in the safety message from thesecond roadway system to determine 1720 an anomaly in the description ofthe safety message from the second roadway system. This anomaly may bebased on a deviation from the description with characteristics observedby the first roadway system using its sensors and perception logic. Thefirst roadway system may initiate one or more remedial actions based onthe anomaly, such as watchlisting or blacklisting the second roadwaysystem, notifying other roadway systems of the anomaly, reportingmisbehavior by the second roadway system to a misbehavior authoritysystem, etc. Indeed, misbehavior data may be generated (and in somecases sent by the first roadway system to other systems) describing theanomaly, including track data, misbehavior reports to other nearbyroadway systems, misbehavior reports for consumption by a certificateauthority, among other examples.

FIGS. 18-19 are block diagrams of exemplary computer architectures thatmay be used in accordance with embodiments disclosed herein. Othercomputer architecture designs known in the art for processors andcomputing systems may also be used. Generally, suitable computerarchitectures for embodiments disclosed herein can include, but are notlimited to, configurations illustrated in FIGS. 18-19.

FIG. 18 is an example illustration of a processor according to anembodiment. Processor 1800 is an example of a type of hardware devicethat can be used in connection with the implementations above. Processor1800 may be any type of processor, such as a microprocessor, an embeddedprocessor, a digital signal processor (DSP), a network processor, amulti-core processor, a single core processor, or other device toexecute code. Although only one processor 1800 is illustrated in FIG.18, a processing element may alternatively include more than one ofprocessor 1800 illustrated in FIG. 18. Processor 1800 may be asingle-threaded core or, for at least one embodiment, the processor 1800may be multi-threaded in that it may include more than one hardwarethread context (or “logical processor”) per core.

FIG. 18 also illustrates a memory 1802 coupled to processor 1800 inaccordance with an embodiment. Memory 1802 may be any of a wide varietyof memories (including various layers of memory hierarchy) as are knownor otherwise available to those of skill in the art. Such memoryelements can include, but are not limited to, random access memory(RAM), read only memory (ROM), logic blocks of a field programmable gatearray (FPGA), erasable programmable read only memory (EPROM), andelectrically erasable programmable ROM (EEPROM).

Processor 1800 can execute any type of instructions associated withalgorithms, processes, or operations detailed herein. Generally,processor 1800 can transform an element or an article (e.g., data) fromone state or thing to another state or thing.

Code 1804, which may be one or more instructions to be executed byprocessor 1800, may be stored in memory 1802, or may be stored insoftware, hardware, firmware, or any suitable combination thereof, or inany other internal or external component, device, element, or objectwhere appropriate and based on particular needs. In one example,processor 1800 can follow a program sequence of instructions indicatedby code 1804. Each instruction enters a front-end logic 1806 and isprocessed by one or more decoders 1808. The decoder may generate, as itsoutput, a micro operation such as a fixed width micro operation in apredefined format, or may generate other instructions,microinstructions, or control signals that reflect the original codeinstruction. Front-end logic 1806 also includes register renaming logic1810 and scheduling logic 1812, which generally allocate resources andqueue the operation corresponding to the instruction for execution.

Processor 1800 can also include execution logic 1814 having a set ofexecution units 1816 a, 1816 b, 1816 n, etc. Some embodiments mayinclude a number of execution units dedicated to specific functions orsets of functions. Other embodiments may include only one execution unitor one execution unit that can perform a particular function. Executionlogic 1814 performs the operations specified by code instructions.

After completion of execution of the operations specified by the codeinstructions, back-end logic 1818 can retire the instructions of code1804. In one embodiment, processor 1800 allows out of order executionbut requires in order retirement of instructions. Retirement logic 1820may take a variety of known forms (e.g., re-order buffers or the like).In this manner, processor 1800 is transformed during execution of code1804, at least in terms of the output generated by the decoder, hardwareregisters and tables utilized by register renaming logic 1810, and anyregisters (not shown) modified by execution logic 1814.

Although not shown in FIG. 18, a processing element may include otherelements on a chip with processor 1800. For example, a processingelement may include memory control logic along with processor 1800. Theprocessing element may include I/O control logic and/or may include I/Ocontrol logic integrated with memory control logic. The processingelement may also include one or more caches. In some embodiments,non-volatile memory (such as flash memory or fuses) may also be includedon the chip with processor 1800.

FIG. 19 illustrates a computing system 1900 that is arranged in apoint-to-point (PtP) configuration according to an embodiment. Inparticular, FIG. 19 shows a system where processors, memory, andinput/output devices are interconnected by a number of point-to-pointinterfaces. Generally, one or more of the computing systems describedherein may be configured in the same or similar manner as computingsystem 1800.

Processors 1970 and 1980 may also each include integrated memorycontroller logic (MC) 1972 and 1982 to communicate with memory elements1932 and 1934. In alternative embodiments, memory controller logic 1972and 1982 may be discrete logic separate from processors 1970 and 1980.Memory elements 1932 and/or 1934 may store various data to be used byprocessors 1970 and 1980 in achieving operations and functionalityoutlined herein.

Processors 1970 and 1980 may be any type of processor, such as thosediscussed in connection with other figures herein. Processors 1970 and1980 may exchange data via a point-to-point (PtP) interface 1950 usingpoint-to-point interface circuits 1978 and 1988, respectively.Processors 1970 and 1980 may each exchange data with a chipset 1990 viaindividual point-to-point interfaces 1952 and 1954 using point-to-pointinterface circuits 1976, 1986, 1994, and 1998. Chipset 1990 may alsoexchange data with a co-processor 1938, such as a high-performancegraphics circuit, machine learning accelerator, or other co-processor1938, via an interface 1939, which could be a PtP interface circuit. Inalternative embodiments, any or all of the PtP links illustrated in FIG.19 could be implemented as a multi-drop bus rather than a PtP link.

Chipset 1990 may be in communication with a bus 1920 via an interfacecircuit 1996. Bus 1920 may have one or more devices that communicateover it, such as a bus bridge 1918 and I/O devices 1916. Via a bus 1910,bus bridge 1918 may be in communication with other devices such as auser interface 1912 (such as a keyboard, mouse, touchscreen, or otherinput devices), communication devices 1926 (such as modems, networkinterface devices, or other types of communication devices that maycommunicate through a computer network 1960), audio I/O devices 1914,and/or a data storage device 1928. Data storage device 1928 may storecode 1930, which may be executed by processors 1970 and/or 1980. Inalternative embodiments, any portions of the bus architectures could beimplemented with one or more PtP links.

The computer system depicted in FIG. 19 is a schematic illustration ofan embodiment of a computing system that may be utilized to implementvarious embodiments discussed herein. It will be appreciated thatvarious components of the system depicted in FIG. 19 may be combined ina system-on-a-chip (SoC) architecture or in any other suitableconfiguration capable of achieving the functionality and features ofexamples and implementations provided herein.

While some of the systems and solutions described and illustrated hereinhave been described as containing or being associated with a pluralityof elements, not all elements explicitly illustrated or described may beutilized in each alternative implementation of the present disclosure.Additionally, one or more of the elements described herein may belocated external to a system, while in other instances, certain elementsmay be included within or as a portion of one or more of the otherdescribed elements, as well as other elements not described in theillustrated implementation. Further, certain elements may be combinedwith other components, as well as used for alternative or additionalpurposes in addition to those purposes described herein.

Further, it should be appreciated that the examples presented above arenon-limiting examples provided merely for purposes of illustratingcertain principles and features and not necessarily limiting orconstraining the potential embodiments of the concepts described herein.For instance, a variety of different embodiments can be realizedutilizing various combinations of the features and components describedherein, including combinations realized through the variousimplementations of components described herein. Other implementations,features, and details should be appreciated from the contents of thisSpecification.

Although this disclosure has been described in terms of certainimplementations and generally associated methods, alterations andpermutations of these implementations and methods will be apparent tothose skilled in the art. For example, the actions described herein canbe performed in a different order than as described and still achievethe desirable results. As one example, the processes depicted in theaccompanying figures do not necessarily require the particular ordershown, or sequential order, to achieve the desired results. In certainimplementations, multitasking and parallel processing may beadvantageous. Additionally, other user interface layouts andfunctionality can be supported. Other variations are within the scope ofthe following claims.

While this specification contains many specific implementation details,these should not be construed as limitations on the scope of anyinventions or of what may be claimed, but rather as descriptions offeatures specific to particular embodiments of particular inventions.Certain features that are described in this specification in the contextof separate embodiments can also be implemented in combination in asingle embodiment. Conversely, various features that are described inthe context of a single embodiment can also be implemented in multipleembodiments separately or in any suitable subcombination. Moreover,although features may be described above as acting in certaincombinations and even initially claimed as such, one or more featuresfrom a claimed combination can in some cases be excised from thecombination, and the claimed combination may be directed to asubcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the embodiments described above should not be understoodas requiring such separation in all embodiments, and it should beunderstood that the described program components and systems cangenerally be integrated together in a single software product orpackaged into multiple software products.

The following examples pertain to embodiments in accordance with thisSpecification. Example 1 is a machine-readable storage medium withinstructions stored thereon, where the instructions are executable by amachine to cause the machine to: receive, at a first roadway system, acommunication from a second roadway system over a wireless channel,where the communication includes an identification of the second roadwaysystem and description of a physical objects within a drivingenvironment; determine characteristics of the physical object based onsensors of the first roadway system; determine that the description ofthe physical object in the communication includes an anomaly based on acomparison with the characteristics of the physical object based onsensors of the first roadway system; generate misbehavior data todescribe the anomaly; and determine whether to initiate a remedialaction based on the anomaly.

Example 2 includes the subject matter of example 1, where themisbehavior data includes a misbehavior report message, and theinstructions are further executable to cause the machine to send themisbehavior report message to another system to initiate at least aportion of the remedial action at the other system.

Example 3 includes the subject matter of example 2, where theidentification of the second roadway system includes credentials of thesecond roadway system to engage in wireless communication with otherroadway systems in the driving environment, the instructions are furtherexecutable to cause the machine to check the credentials of the secondroadway system in association with receipt of the communication, and theother system includes a misbehavior authority system to determinewhether the credentials of the second roadway system are to be revokedbased on misbehavior associated with the anomaly.

Example 4 includes the subject matter of example 2, where the othersystem includes another roadway system in the driving environment, andthe misbehavior data identifies to the other roadway system that theanomaly was detected in the communication from the second roadwaysystem.

Example 5 includes the subject matter of example 2, where theinstructions are further executable to cause the machine to receiveanother misbehavior report message from another roadway system, theother misbehavior report identifies an anomaly detected by the otherroadway system involving a communication from the second roadway systemat the other roadway system, and the remediate action is furtherdetermined based on the other misbehavior report.

Example 6 includes the subject matter of example 5, where theinstructions are to further executable to determine whether a thresholdof misbehavior reports has been met and the remedial action is based atleast in part on whether the threshold is met.

Example 7 includes the subject matter of any one of examples 1-6, wherethe remedial action includes addition of the second roadway system to awatchlist, additional remedial action is to be initiated when anomaliesare detected by the first roadway system in subsequent communicationsfrom the second roadway system based on inclusion of the second roadwaysystem in the watchlist.

Example 8 includes the subject matter of any one of examples 1-7, wherethe remedial action includes adding the identifier of the second roadwaysystem to a local blacklist, the instructions are further executable tocause the machine to: scan communications received by the first roadwaysystem to determine whether sender identifiers are included in the localblacklist; and disregard communications from senders with senderidentifier included in the local blacklist.

Example 9 includes the subject matter of any one of examples 1-8, wherethe instructions are further executable to cause the machine todetermine a degree of divergence in the description of the physicalobject in the communication from the characteristics of the physicalobject based on sensors of the first roadway system, where theinitiation of the remedial action is based on the degree of divergence.

Example 10 includes the subject matter of example 9, where theinstructions are further executable to determine an amount of anomaliesin communications from the second roadway system during a time window,determining whether to initiate the remedial action is based on whetherthe amount of anomalies exceeds a threshold, and duration of the timewindow is based on the degree of divergence.

Example 11 includes the subject matter of any one of examples 1-10,where the communication includes a safety message, the first roadwaysystem is to direct autonomous driving of a vehicle, and the firstroadway system is to direct the autonomous driving based at least inpart on safety messages received from other roadway systems in thedriving environment.

Example 12 includes the subject matter of example 11, where theinstructions are further executable to cause the machine to screen thesafety message to determine whether a position of the physical object iswithin an assumed range. message screened for assumptions

Example 13 includes the subject matter of any one of examples 11-12,where at least one other safety message is received from another roadwaysystem and describes the physical object, and the anomaly is furtherbased on a comparison of contents of the safety message with informationin the other safety message.

Example 14 includes the subject matter of any one of examples 11-13,where the safety message is a second safety message received from thesecond roadway system after receipt of a first safety message from thesecond roadway system, the first safety message also describes thephysical object, and the instructions are further executable to causethe machine to: determine, from a model, an expected state of thephysical object based on the first safety message; and determine whetherthe description of the physical object in the second safety messagediverges from the expected state, where the anomaly is further based ondetermination of a divergence between the description of the physicalobject and the expected state.

Example 15 includes the subject matter of any one of examples 1-14,where the instructions are further executable to generate track data totrack the physical object.

Example 16 includes the subject matter of example 15, where respectivetrack data is maintained for each one of a plurality of physical objectsdetected or reported within the driving environment.

Example 17 includes the subject matter of any one of examples 15-16,where the track data is labeled as a reported object track (VT), aperceived object track (LT), or a mixed track (MT) including bothreported and perceived information.

Example 18 includes the subject matter of example 17, where theinstructions are further executable to cause the machine to: identifyexisting track data for the physical object, where the existing trackdata includes a mixed track based at least in part on a previouslyreceived communication describing the physical object; and changedesignation of the existing track date from MT to LT based on theanomaly.

Example 19 includes the subject matter of any one of examples 15-17,where the track data is to merge information for physical objectsobtained either or both internally by the first roadway system or fromother roadway systems.

Example 20 includes the subject matter of any one of examples 1-19,where each of the first and second roadway systems include a respectiveone of an autonomous vehicle or a roadside unit.

Example 21 is a method including: receiving, at a first roadway system,a communication from a second roadway system over a wireless channel,where the communication includes an identification of the second roadwaysystem and description of a physical objects within a drivingenvironment; determining characteristics of the physical object based onsensors of the first roadway system; determining that the description ofthe physical object in the communication includes an anomaly based on acomparison with the characteristics of the physical object based onsensors of the first roadway system; generating misbehavior data todescribe the anomaly; and determining whether to initiate a remedialaction based on the anomaly, where the remedial action is to prevent thefirst roadway system from considering subsequent anomalouscommunications.

Example 22 includes the subject matter of example 21, further includingsending the misbehavior data to another system as a misbehavior reportmessage, where the misbehavior report message reports potentialmisbehavior by the second roadway system based on the anomaly.

Example 23 includes the subject matter of any one of examples 21-22,where the misbehavior data includes a misbehavior report message, andthe method further includes sending the misbehavior report message toanother system to initiate at least a portion of the remedial action atthe other system.

Example 24 includes the subject matter of example 23, where theidentification of the second roadway system includes credentials of thesecond roadway system to engage in wireless communication with otherroadway systems in the driving environment, the method further includeschecking the credentials of the second roadway system in associationwith receipt of the communication, and the other system includes amisbehavior authority system to determine whether the credentials of thesecond roadway system are to be revoked based on misbehavior associatedwith the anomaly.

Example 25 includes the subject matter of any one of examples 23-24,where the other system includes another roadway system in the drivingenvironment, and the misbehavior data identifies to the other roadwaysystem that the anomaly was detected in the communication from thesecond roadway system.

Example 26 includes the subject matter of any one of examples 23-25,further including receiving another misbehavior report message fromanother roadway system, the other misbehavior report identifies ananomaly detected by the other roadway system involving a communicationfrom the second roadway system at the other roadway system, and theremediate action is further determined based on the other misbehaviorreport.

Example 27 includes the subject matter of example 26, further includingdetermining whether a threshold of misbehavior reports has been met andthe remedial action is based at least in part on whether the thresholdis met.

Example 28 includes the subject matter of any one of examples 21-27,where the remedial action includes addition of the second roadway systemto a watchlist, additional remedial action is to be initiated whenanomalies are detected by the first roadway system in subsequentcommunications from the second roadway system based on inclusion of thesecond roadway system in the watchlist.

Example 29 includes the subject matter of any one of examples 21-28,where the remedial action includes adding the identifier of the secondroadway system to a local blacklist, and the method further includes:scanning communications received by the first roadway system todetermine whether sender identifiers are included in the localblacklist; and disregarding communications from senders with senderidentifier included in the local blacklist.

Example 30 includes the subject matter of any one of examples 21-29,further including determining a degree of divergence in the descriptionof the physical object in the communication from the characteristics ofthe physical object based on sensors of the first roadway system, wherethe initiation of the remedial action is based on the degree ofdivergence.

Example 31 includes the subject matter of example 30, further includingdetermining an amount of anomalies in communications from the secondroadway system during a time window, determining whether to initiate theremedial action is based on whether the amount of anomalies exceeds athreshold, and duration of the time window is based on the degree ofdivergence.

Example 32 includes the subject matter of any one of examples 21-31,where the communication includes a safety message, the first roadwaysystem is to direct autonomous driving of a vehicle, and the firstroadway system is to direct the autonomous driving based at least inpart on safety messages received from other roadway systems in thedriving environment.

Example 33 includes the subject matter of example 32, further includingscreening the safety message to determine whether a position of thephysical object is within an assumed range. message screened forassumptions

Example 34 includes the subject matter of any one of examples 32-33,where at least one other safety message is received from another roadwaysystem and describes the physical object, and the anomaly is furtherbased on a comparison of contents of the safety message with informationin the other safety message.

Example 35 includes the subject matter of any one of examples 32-34,where the safety message is a second safety message received from thesecond roadway system after receipt of a first safety message from thesecond roadway system, the first safety message also describes thephysical object, and the method further includes: determining, from amodel, an expected state of the physical object based on the firstsafety message; and determining whether the description of the physicalobject in the second safety message diverges from the expected state,where the anomaly is further based on determination of a divergencebetween the description of the physical object and the expected state.

Example 36 includes the subject matter of any one of examples 21-35,further including generating track data to track the physical object.

Example 37 includes the subject matter of example 36, where respectivetrack data is maintained for each one of a plurality of physical objectsdetected or reported within the driving environment.

Example 38 includes the subject matter of any one of examples 36-37,where the track data is labeled as a reported object track (VT), aperceived object track (LT), or a mixed track (MT) including bothreported and perceived information.

Example 39 includes the subject matter of example 38, further including:identify existing track data for the physical object, where the existingtrack data includes a mixed track based at least in part on a previouslyreceived communication describing the physical object; and changedesignation of the existing track date from MT to LT based on theanomaly.

Example 40 includes the subject matter of any one of examples 37-39,where the track data is to merge information for physical objectsobtained either or both internally by the first roadway system or fromother roadway systems.

Example 41 includes the subject matter of any one of examples 21-40,where each of the first and second roadway systems include a respectiveone of an autonomous vehicle or a roadside unit.

Example 42 is a system including means to perform the method of any oneof examples 21-41.

Example 43 is a system including: one or more data processors; a memory;a set of sensors; a perception engine executable by at least one of theone or more data processors to use sensor data generated by the set ofsensors to determine characteristics of physical objects within adriving environment; and a misbehavior detection engine executable by atleast one of the one or more data processors to: identify a descriptionof a particular physical object in a safety message received from aparticular roadway system; compare characteristics of the particularphysical object determined by the perception engine based on the sensordata to determine an anomaly in the description; determine whether thesafety message includes misbehavior by the particular roadway systembased at least in part on the anomaly; and initiate remedial actionbased on the anomaly.

Example 44 includes the subject matter of example 43, where themisbehavior detection engine is further to generate misbehavior databased on the anomaly.

Example 45 includes the subject matter of example 44, where themisbehavior data includes a misbehavior report message, and themisbehavior detection engine is to send the misbehavior report messageto another system to initiate at least a portion of the remedial actionat the other system.

Example 46 includes the subject matter of example 45, where the safetymessage includes credentials of the particular roadway system to engagein safety message communication with other roadway systems in thedriving environment, the misbehavior detection engine is to check thecredentials of the particular roadway system in association with receiptof the safety message, and the other system includes a misbehaviorauthority system to determine whether the credentials of the particularroadway system are to be revoked based on misbehavior associated withthe anomaly.

Example 47 includes the subject matter of example 45, where the othersystem includes another roadway system in the driving environment, andthe misbehavior data identifies to the other roadway system that theanomaly was detected in the communication from the particular roadwaysystem.

Example 48 includes the subject matter of example 45, where themisbehavior detection engine is further to receive another misbehaviorreport message from another roadway system, the other misbehavior reportidentifies an anomaly detected by the other roadway system involvinganother safety message from the particular roadway system at the otherroadway system, and the remedial action is further determined based onthe other misbehavior report.

Example 49 includes the subject matter of example 48, where theinstructions are to further executable to determine whether a thresholdof misbehavior reports has been met and the remedial action is based atleast in part on whether the threshold is met.

Example 50 includes the subject matter of any one of examples 43-49,where the remedial action includes addition of the particular roadwaysystem to a watchlist, additional remedial action is to be initiatedwhen anomalies are detected by the first roadway system in subsequentsafety messages from the particular roadway system based on inclusion ofthe particular roadway system in the watchlist.

Example 51 includes the subject matter of any one of examples 43-50,where the remedial action includes adding the identifier of theparticular roadway system to a local blacklist, the misbehaviordetection engine is further to: scan received safety messages todetermine whether sender identifiers are included in the localblacklist; and disregard communications from senders with senderidentifier included in the local blacklist.

Example 52 includes the subject matter of any one of examples 43-51,where the misbehavior detection engine is further to determine a degreeof divergence in the description of the physical object in thecommunication from the characteristics of the physical object based onsensors of the first roadway system, where the initiation of theremedial action is based on the degree of divergence.

Example 53 includes the subject matter of example 52, where theinstructions are further executable to determine an amount of anomaliesin communications from the particular roadway system during a timewindow, determining whether to initiate the remedial action is based onwhether the amount of anomalies exceeds a threshold, and duration of thetime window is based on the degree of divergence.

Example 54 includes the subject matter of any one of examples 43-53,further including autonomous driving controls to direct autonomousdriving of a vehicle, and the autonomous driving controls are to directthe autonomous driving based at least in part on safety messagesreceived from other roadway systems in the driving environment.

Example 55 includes the subject matter of example 54, where themisbehavior detection engine is further to screen the safety message todetermine whether a position of the physical object is within an assumedrange. message screened for assumptions

Example 56 includes the subject matter of any one of examples 54-55,where at least one other safety message is received from another roadwaysystem and describes the physical object, and the anomaly is furtherbased on a comparison of contents of the safety message with informationin the other safety message.

Example 57 includes the subject matter of any one of examples 54-56,where the safety message is a second safety message received from theparticular roadway system after receipt of a first safety message fromthe particular roadway system, the first safety message also describesthe physical object, and the misbehavior detection engine is further to:determine, from a model, an expected state of the physical object basedon the first safety message; and determine whether the description ofthe physical object in the second safety message diverges from theexpected state, where the anomaly is further based on determination of adivergence between the description of the physical object and theexpected state.

Example 58 includes the subject matter of any one of examples 43-57,where the instructions are further executable to generate track data totrack the physical object.

Example 59 includes the subject matter of example 58, where respectivetrack data is maintained for each one of a plurality of physical objectsdetected or reported within the driving environment.

Example 60 includes the subject matter of any one of examples 58-59,where the track data is labeled as a reported object track (VT), aperceived object track (LT), or a mixed track (MT) including bothreported and perceived information.

Example 61 includes the subject matter of example 60, where themisbehavior detection engine is further to: identify existing track datafor the physical object, where the existing track data includes a mixedtrack based at least in part on a previously received communicationdescribing the physical object; and change designation of the existingtrack date from MT to LT based on the anomaly.

Example 62 includes the subject matter of any one of examples 59-61,where the track data is to merge information for physical objectsobtained from either or both the perception engine or from other roadwaysystems.

Example 63 includes the subject matter of any one of examples 43-61,where the perception engine utilizes a machine learning model andprovides the sensor data as inputs to the machine learning model todetect the physical objects, and the perception engine is further totrack movement of the physical objects within the driving environment.

Example 64 includes the subject matter of any one of examples 43-63,where the system includes an at least partially autonomous vehicle tomove within the driving environment and the vehicle includes acontroller to control movement of the vehicle based at least in part onsafety messages received from other systems within the drivingenvironment.

Example 65 includes the subject matter of any one of examples 43-64,where the system includes roadside equipment to monitor the drivingenvironment.

Thus, particular embodiments of the subject matter have been described.Other embodiments are within the scope of the following claims. In somecases, the actions recited in the claims can be performed in a differentorder and still achieve desirable results. In addition, the processesdepicted in the accompanying figures do not necessarily require theparticular order shown, or sequential order, to achieve desirableresults.

1. At least one machine-readable storage medium with instructions stored thereon, wherein the instructions are executable by a machine to cause the machine to: receive, at a first roadway system, a communication from a second roadway system over a wireless channel, wherein the communication comprises an identification of the second roadway system and description of a physical object within a driving environment; determine characteristics of the physical object based on sensors of the first roadway system; determine that the description of the physical object in the communication comprises an anomaly based on a comparison with the characteristics; generate misbehavior data to describe the anomaly; and determine whether to initiate a remedial action based on the anomaly.
 2. The storage medium of claim 1, wherein the misbehavior data comprises a misbehavior report message, and the instructions are further executable to cause the machine to send the misbehavior report message to one or more other systems to initiate at least a portion of the remedial action at the other system.
 3. The storage medium of claim 2, wherein the identification of the second roadway system comprises credentials of the second roadway system to engage in wireless communication with other roadway systems in the driving environment, the instructions are further executable to cause the machine to check the credentials of the second roadway system in association with receipt of the communication, and the one or more other systems comprise a misbehavior authority system to determine whether to revoke the credentials of the second roadway system based on misbehavior associated with the anomaly.
 4. The storage medium of claim 2, wherein the one or more other systems comprise a set of roadway system in the driving environment within range of the first roadway system, and the misbehavior data identifies to the set of roadway systems that the anomaly was detected in the communication from the second roadway system.
 5. The storage medium of claim 2, wherein the instructions are further executable to cause the machine to receive another misbehavior report message from another roadway system, the other misbehavior report identifies an anomaly detected by the other roadway system involving a communication from the second roadway system at the other roadway system, and the remedial action is further determined based on the received other misbehavior report.
 6. The storage medium of claim 1, wherein the remedial action comprises addition of the second roadway system to a watchlist, and subsequent communications from the second roadway system are to be monitored based on inclusion of the second roadway system in the watchlist to determine whether additional remedial action is to be initiated.
 7. The storage medium of claim 1, wherein the remedial action comprises adding the identifier of the second roadway system to a local blacklist, the instructions are further executable to cause the machine to: scan communications received by the first roadway system to determine whether sender identifiers are included in the local blacklist; and disregard communications from senders with sender identifier included in the local blacklist.
 8. The storage medium of claim 1, wherein the instructions are further executable to cause the machine to determine a degree of divergence in the description of the physical object in the communication from the characteristics, wherein the initiation of the remedial action is based on the degree of divergence.
 9. The storage medium of claim 8, wherein the instructions are further executable to determine an amount of anomalies in communications from the second roadway system during a time window, determining whether to initiate the remedial action is based on whether the amount of anomalies exceeds a threshold, and duration of the time window is based on the degree of divergence.
 10. The storage medium of claim 1, wherein the communication comprises a safety message, the first roadway system is to direct autonomous driving of a vehicle, and the first roadway system is to direct the autonomous driving based at least in part on safety messages received from other roadway systems in the driving environment.
 11. The storage medium of claim 10, wherein the instructions are further executable to cause the machine to screen the safety message to determine whether a position of the physical object is within an assumed range.
 12. The storage medium of claim 10, wherein at least one other safety message is received from another roadway system and describes the physical object, and the anomaly is further based on a comparison of contents of the safety message with information in the other safety message.
 13. The storage medium of claim 10, wherein the safety message is a second safety message received from the second roadway system after receipt of a first safety message from the second roadway system, the first safety message also describes the physical object, and the instructions are further executable to cause the machine to: determine, from a model, an expected state of the physical object based on the first safety message; determine whether the description of the physical object in the second safety message diverges from the expected state, wherein the anomaly is further based on determination of a divergence between the description of the physical object and the expected state.
 14. The storage medium of claim 1, wherein each of the first and second roadway systems comprise a respective one of an autonomous vehicle or a roadside unit.
 15. A method comprising: receiving, at a first roadway system, a communication from a second roadway system over a wireless channel, wherein the communication comprises an identification of the second roadway system and description of a physical object within a driving environment; determining characteristics of the physical object based on sensors of the first roadway system; determining that the description of the physical object in the communication comprises an anomaly based on a comparison with the characteristics; generating misbehavior data to describe the anomaly; and determining whether to initiate a remedial action based on the anomaly, wherein the remedial action is to prevent the first roadway system from considering subsequent anomalous communications.
 16. The method of claim 15, further comprising sending the misbehavior data to another system as a misbehavior report message, wherein the misbehavior report message reports potential misbehavior by the second roadway system based on the anomaly.
 17. A system comprising: one or more data processors; a memory; a set of sensors; a perception engine executable by at least one of the one or more data processors to use sensor data generated by the set of sensors to determine characteristics of physical objects within a driving environment; and a misbehavior detection engine executable by at least one of the one or more data processors to: identify a description of a particular physical object in a safety message received from a particular roadway system; compare characteristics of the particular physical object determined by the perception engine based on the sensor data to determine an anomaly in the description; determine whether the safety message comprises misbehavior by the particular roadway system based at least in part on the anomaly; and initiate remedial action based on the anomaly.
 18. The system of claim 17, wherein the perception engine utilizes a machine learning model and provides the sensor data as inputs to the machine learning model to detect the physical objects, and the perception engine is further to track movement of the physical objects within the driving environment.
 19. The system of claim 17, wherein the system comprises an at least partially autonomous vehicle to move within the driving environment, and the vehicle comprises a controller to control movement of the vehicle based at least in part on safety messages received from other systems within the driving environment.
 20. The system of claim 17, wherein the system comprises roadside equipment to monitor the driving environment. 